Overview

Dataset statistics

Number of variables149
Number of observations8499
Missing cells935362
Missing cells (%)73.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 MiB
Average record size in memory1.2 KiB

Variable types

Text2
Numeric2
Categorical139
Unsupported6

Alerts

MCIN1VIS has constant value ""Constant
MCIN2VIS has constant value ""Constant
SEMDEMAN has constant value ""Constant
SEMDEMAG has constant value ""Constant
PSPIF has constant value ""Constant
CORTIF has constant value ""Constant
HUNT has constant value ""Constant
PRION has constant value ""Constant
DOWNS has constant value ""Constant
DOWNSIF has constant value ""Constant
hiv has constant value ""Constant
amylpet has constant value ""Constant
amylcsf has constant value ""Constant
fdgad has constant value ""Constant
hippatr has constant value ""Constant
taupetad has constant value ""Constant
csftau has constant value ""Constant
fdgftld has constant value ""Constant
tpetftld has constant value ""Constant
mrftld has constant value ""Constant
datscan has constant value ""Constant
othbiom has constant value ""Constant
imaglinf has constant value ""Constant
imaglac has constant value ""Constant
imagmach has constant value ""Constant
imagmich has constant value ""Constant
imagmwmh has constant value ""Constant
imagewmh has constant value ""Constant
admut has constant value ""Constant
ftldmut has constant value ""Constant
othmut has constant value ""Constant
msa has constant value ""Constant
ftldmo has constant value ""Constant
neopstat has constant value ""Constant
delir has constant value ""Constant
ptsddxif has constant value ""Constant
days_to_visit is highly overall correlated with PPAPHIF and 6 other fieldsHigh correlation
age at visit is highly overall correlated with MCIN1LAN and 5 other fieldsHigh correlation
WHODIDDX is highly overall correlated with MCIAPLUS and 29 other fieldsHigh correlation
NORMCOG is highly overall correlated with MCIN1LAN and 34 other fieldsHigh correlation
DEMENTED is highly overall correlated with MCIN1LAN and 29 other fieldsHigh correlation
MCIAMEM is highly overall correlated with MCIN1LAN and 27 other fieldsHigh correlation
MCIAPLUS is highly overall correlated with WHODIDDX and 29 other fieldsHigh correlation
MCIAPLAN is highly overall correlated with WHODIDDX and 31 other fieldsHigh correlation
MCIAPATT is highly overall correlated with WHODIDDX and 29 other fieldsHigh correlation
MCIAPEX is highly overall correlated with WHODIDDX and 27 other fieldsHigh correlation
MCIAPVIS is highly overall correlated with WHODIDDX and 32 other fieldsHigh correlation
MCINON1 is highly overall correlated with MCIAPLAN and 42 other fieldsHigh correlation
MCIN1LAN is highly overall correlated with age at visit and 54 other fieldsHigh correlation
MCIN1ATT is highly overall correlated with WHODIDDX and 41 other fieldsHigh correlation
MCIN1EX is highly overall correlated with WHODIDDX and 38 other fieldsHigh correlation
MCINON2 is highly overall correlated with MCIAPLAN and 40 other fieldsHigh correlation
MCIN2LAN is highly overall correlated with WHODIDDX and 47 other fieldsHigh correlation
MCIN2ATT is highly overall correlated with WHODIDDX and 47 other fieldsHigh correlation
MCIN2EX is highly overall correlated with WHODIDDX and 48 other fieldsHigh correlation
IMPNOMCI is highly overall correlated with MCIAPLUS and 27 other fieldsHigh correlation
PROBAD is highly overall correlated with DEMENTED and 16 other fieldsHigh correlation
PROBADIF is highly overall correlated with WHODIDDX and 23 other fieldsHigh correlation
POSSAD is highly overall correlated with MCIN1LAN and 10 other fieldsHigh correlation
POSSADIF is highly overall correlated with MCIAPVIS and 21 other fieldsHigh correlation
DLB is highly overall correlated with MCIN1LAN and 21 other fieldsHigh correlation
DLBIF is highly overall correlated with WHODIDDX and 24 other fieldsHigh correlation
VASC is highly overall correlated with MCIN1LAN and 23 other fieldsHigh correlation
VASCIF is highly overall correlated with WHODIDDX and 26 other fieldsHigh correlation
VASCPS is highly overall correlated with MCIN1LAN and 17 other fieldsHigh correlation
VASCPSIF is highly overall correlated with WHODIDDX and 27 other fieldsHigh correlation
ALCDEM is highly overall correlated with MCIAPLAN and 24 other fieldsHigh correlation
ALCDEMIF is highly overall correlated with WHODIDDX and 43 other fieldsHigh correlation
DEMUN is highly overall correlated with MCIN1LAN and 14 other fieldsHigh correlation
DEMUNIF is highly overall correlated with WHODIDDX and 28 other fieldsHigh correlation
FTD is highly overall correlated with MCIAPLAN and 27 other fieldsHigh correlation
FTDIF is highly overall correlated with age at visit and 28 other fieldsHigh correlation
PPAPH is highly overall correlated with MCIAMEM and 33 other fieldsHigh correlation
PPAPHIF is highly overall correlated with days_to_visit and 23 other fieldsHigh correlation
PNAPH is highly overall correlated with days_to_visit and 23 other fieldsHigh correlation
PPAOTHR is highly overall correlated with days_to_visit and 24 other fieldsHigh correlation
PSP is highly overall correlated with WHODIDDX and 65 other fieldsHigh correlation
CORT is highly overall correlated with MCIAMEM and 77 other fieldsHigh correlation
MEDS is highly overall correlated with MCIN2LAN and 19 other fieldsHigh correlation
MEDSIF is highly overall correlated with WHODIDDX and 39 other fieldsHigh correlation
DYSILL is highly overall correlated with MCIN1LAN and 29 other fieldsHigh correlation
DYSILLIF is highly overall correlated with WHODIDDX and 36 other fieldsHigh correlation
DEP is highly overall correlated with NEOPIF and 4 other fieldsHigh correlation
DEPIF is highly overall correlated with MCIN1LAN and 24 other fieldsHigh correlation
OTHPSY is highly overall correlated with MCIN1LAN and 28 other fieldsHigh correlation
OTHPSYIF is highly overall correlated with WHODIDDX and 40 other fieldsHigh correlation
PARK is highly overall correlated with MCIN2LAN and 31 other fieldsHigh correlation
STROKE is highly overall correlated with MCIN1LAN and 14 other fieldsHigh correlation
STROKIF is highly overall correlated with WHODIDDX and 22 other fieldsHigh correlation
HYCEPH is highly overall correlated with MCIAMEM and 63 other fieldsHigh correlation
HYCEPHIF is highly overall correlated with days_to_visit and 17 other fieldsHigh correlation
BRNINJ is highly overall correlated with MCIN1LAN and 25 other fieldsHigh correlation
BRNINJIF is highly overall correlated with MCINON1 and 37 other fieldsHigh correlation
NEOP is highly overall correlated with MCIAPLAN and 50 other fieldsHigh correlation
NEOPIF is highly overall correlated with WHODIDDX and 26 other fieldsHigh correlation
COGOTH is highly overall correlated with DEMUNIF and 16 other fieldsHigh correlation
COGOTHIF is highly overall correlated with WHODIDDX and 34 other fieldsHigh correlation
COGOTH2 is highly overall correlated with MCIN1LAN and 32 other fieldsHigh correlation
COGOTH2F is highly overall correlated with WHODIDDX and 36 other fieldsHigh correlation
COGOTH3 is highly overall correlated with MCIN1LAN and 48 other fieldsHigh correlation
COGOTH3F is highly overall correlated with days_to_visit and 39 other fieldsHigh correlation
dxmethod is highly overall correlated with MCIAPLAN and 39 other fieldsHigh correlation
amndem is highly overall correlated with NORMCOG and 29 other fieldsHigh correlation
pca is highly overall correlated with NORMCOG and 31 other fieldsHigh correlation
ppasyn is highly overall correlated with NORMCOG and 33 other fieldsHigh correlation
ppasynt is highly overall correlated with NORMCOG and 37 other fieldsHigh correlation
ftdsyn is highly overall correlated with NORMCOG and 32 other fieldsHigh correlation
lbdsyn is highly overall correlated with NORMCOG and 34 other fieldsHigh correlation
namndem is highly overall correlated with NORMCOG and 34 other fieldsHigh correlation
alzdis is highly overall correlated with NORMCOG and 16 other fieldsHigh correlation
alzdisif is highly overall correlated with MCINON1 and 27 other fieldsHigh correlation
lbdis is highly overall correlated with MCIN1LAN and 33 other fieldsHigh correlation
lbdif is highly overall correlated with NORMCOG and 28 other fieldsHigh correlation
ftldnos is highly overall correlated with MCIN1LAN and 28 other fieldsHigh correlation
ftldnoif is highly overall correlated with NORMCOG and 35 other fieldsHigh correlation
ftldsubt is highly overall correlated with NORMCOG and 37 other fieldsHigh correlation
cvd is highly overall correlated with MCIN1LAN and 22 other fieldsHigh correlation
cvdif is highly overall correlated with MCIAMEM and 32 other fieldsHigh correlation
prevstk is highly overall correlated with MCIAPLAN and 30 other fieldsHigh correlation
strokdec is highly overall correlated with NORMCOG and 35 other fieldsHigh correlation
stkimag is highly overall correlated with MCIAMEM and 39 other fieldsHigh correlation
infnetw is highly overall correlated with DEMENTED and 43 other fieldsHigh correlation
infwmh is highly overall correlated with DEMENTED and 43 other fieldsHigh correlation
esstrem is highly overall correlated with MCIN1LAN and 13 other fieldsHigh correlation
esstreif is highly overall correlated with MCIAPLAN and 37 other fieldsHigh correlation
brnincte is highly overall correlated with MCINON1 and 28 other fieldsHigh correlation
epilep is highly overall correlated with MCIAPLAN and 26 other fieldsHigh correlation
epilepif is highly overall correlated with MCIAPLUS and 31 other fieldsHigh correlation
othcog is highly overall correlated with MCIN1LAN and 27 other fieldsHigh correlation
othcogif is highly overall correlated with NORMCOG and 35 other fieldsHigh correlation
deptreat is highly overall correlated with MCIN1LAN and 25 other fieldsHigh correlation
bipoldx is highly overall correlated with MCIAPLAN and 19 other fieldsHigh correlation
bipoldif is highly overall correlated with NORMCOG and 31 other fieldsHigh correlation
schizop is highly overall correlated with MCIAPLAN and 37 other fieldsHigh correlation
schizoif is highly overall correlated with days_to_visit and 30 other fieldsHigh correlation
anxiet is highly overall correlated with MCIN1LAN and 9 other fieldsHigh correlation
anxietif is highly overall correlated with NORMCOG and 41 other fieldsHigh correlation
ptsddx is highly overall correlated with DEMENTED and 51 other fieldsHigh correlation
alcabuse is highly overall correlated with NORMCOG and 32 other fieldsHigh correlation
impsub is highly overall correlated with MCIN1LAN and 29 other fieldsHigh correlation
impsubif is highly overall correlated with days_to_visit and 39 other fieldsHigh correlation
WHODIDDX is highly imbalanced (99.1%)Imbalance
MCIAPVIS is highly imbalanced (52.5%)Imbalance
MCINON1 is highly imbalanced (80.1%)Imbalance
MCIN1LAN is highly imbalanced (67.7%)Imbalance
MCINON2 is highly imbalanced (84.3%)Imbalance
PROBADIF is highly imbalanced (91.6%)Imbalance
DLB is highly imbalanced (82.3%)Imbalance
VASC is highly imbalanced (89.3%)Imbalance
VASCPS is highly imbalanced (90.5%)Imbalance
VASCPSIF is highly imbalanced (56.1%)Imbalance
ALCDEM is highly imbalanced (94.7%)Imbalance
DEMUN is highly imbalanced (76.7%)Imbalance
DEMUNIF is highly imbalanced (54.6%)Imbalance
FTD is highly imbalanced (91.0%)Imbalance
FTDIF is highly imbalanced (60.9%)Imbalance
PPAPH is highly imbalanced (95.3%)Imbalance
PSP is highly imbalanced (99.7%)Imbalance
CORT is highly imbalanced (99.8%)Imbalance
MEDS is highly imbalanced (91.1%)Imbalance
DYSILL is highly imbalanced (91.1%)Imbalance
OTHPSY is highly imbalanced (94.6%)Imbalance
PARK is highly imbalanced (90.6%)Imbalance
STROKE is highly imbalanced (87.8%)Imbalance
HYCEPH is highly imbalanced (99.4%)Imbalance
BRNINJ is highly imbalanced (91.2%)Imbalance
NEOP is highly imbalanced (98.9%)Imbalance
COGOTH is highly imbalanced (81.8%)Imbalance
COGOTH2 is highly imbalanced (97.1%)Imbalance
COGOTH3 is highly imbalanced (99.3%)Imbalance
dxmethod is highly imbalanced (99.3%)Imbalance
amndem is highly imbalanced (85.1%)Imbalance
pca is highly imbalanced (93.9%)Imbalance
ppasyn is highly imbalanced (91.5%)Imbalance
ftdsyn is highly imbalanced (96.6%)Imbalance
lbdsyn is highly imbalanced (91.5%)Imbalance
namndem is highly imbalanced (98.1%)Imbalance
alzdisif is highly imbalanced (91.6%)Imbalance
lbdis is highly imbalanced (96.1%)Imbalance
ftldnos is highly imbalanced (98.5%)Imbalance
cvd is highly imbalanced (87.4%)Imbalance
stkimag is highly imbalanced (50.5%)Imbalance
infnetw is highly imbalanced (79.7%)Imbalance
infwmh is highly imbalanced (79.7%)Imbalance
esstrem is highly imbalanced (65.5%)Imbalance
esstreif is highly imbalanced (85.0%)Imbalance
epilep is highly imbalanced (95.1%)Imbalance
othcog is highly imbalanced (95.7%)Imbalance
bipoldx is highly imbalanced (94.7%)Imbalance
schizop is highly imbalanced (98.8%)Imbalance
anxiet is highly imbalanced (81.6%)Imbalance
ptsddx is highly imbalanced (99.3%)Imbalance
alcabuse is highly imbalanced (58.5%)Imbalance
impsub is highly imbalanced (98.5%)Imbalance
WHODIDDX has 4477 (52.7%) missing valuesMissing
NORMCOG has 837 (9.8%) missing valuesMissing
DEMENTED has 6623 (77.9%) missing valuesMissing
MCIAMEM has 8020 (94.4%) missing valuesMissing
MCIAPLUS has 8021 (94.4%) missing valuesMissing
MCIAPLAN has 8391 (98.7%) missing valuesMissing
MCIAPATT has 8390 (98.7%) missing valuesMissing
MCIAPEX has 8386 (98.7%) missing valuesMissing
MCIAPVIS has 8391 (98.7%) missing valuesMissing
MCINON1 has 8015 (94.3%) missing valuesMissing
MCIN1LAN has 8482 (99.8%) missing valuesMissing
MCIN1ATT has 8483 (99.8%) missing valuesMissing
MCIN1EX has 8480 (99.8%) missing valuesMissing
MCIN1VIS has 8483 (99.8%) missing valuesMissing
MCINON2 has 8018 (94.3%) missing valuesMissing
MCIN2LAN has 8486 (99.8%) missing valuesMissing
MCIN2ATT has 8486 (99.8%) missing valuesMissing
MCIN2EX has 8480 (99.8%) missing valuesMissing
MCIN2VIS has 8486 (99.8%) missing valuesMissing
IMPNOMCI has 8015 (94.3%) missing valuesMissing
PROBAD has 7368 (86.7%) missing valuesMissing
PROBADIF has 7832 (92.2%) missing valuesMissing
POSSAD has 8041 (94.6%) missing valuesMissing
POSSADIF has 8304 (97.7%) missing valuesMissing
DLB has 7367 (86.7%) missing valuesMissing
DLBIF has 8471 (99.7%) missing valuesMissing
VASC has 7367 (86.7%) missing valuesMissing
VASCIF has 8483 (99.8%) missing valuesMissing
VASCPS has 7593 (89.3%) missing valuesMissing
VASCPSIF has 8488 (99.9%) missing valuesMissing
ALCDEM has 3723 (43.8%) missing valuesMissing
ALCDEMIF has 8472 (99.7%) missing valuesMissing
DEMUN has 7367 (86.7%) missing valuesMissing
DEMUNIF has 8457 (99.5%) missing valuesMissing
FTD has 7366 (86.7%) missing valuesMissing
FTDIF has 8486 (99.8%) missing valuesMissing
PPAPH has 7358 (86.6%) missing valuesMissing
PPAPHIF has 8494 (99.9%) missing valuesMissing
PNAPH has 8493 (99.9%) missing valuesMissing
SEMDEMAN has 8494 (99.9%) missing valuesMissing
SEMDEMAG has 8493 (99.9%) missing valuesMissing
PPAOTHR has 8493 (99.9%) missing valuesMissing
PSP has 832 (9.8%) missing valuesMissing
PSPIF has 8497 (> 99.9%) missing valuesMissing
CORT has 832 (9.8%) missing valuesMissing
CORTIF has 8498 (> 99.9%) missing valuesMissing
HUNT has 832 (9.8%) missing valuesMissing
HUNTIF has 8499 (100.0%) missing valuesMissing
PRION has 832 (9.8%) missing valuesMissing
PRIONIF has 8499 (100.0%) missing valuesMissing
MEDS has 835 (9.8%) missing valuesMissing
MEDSIF has 8426 (99.1%) missing valuesMissing
DYSILL has 834 (9.8%) missing valuesMissing
DYSILLIF has 8428 (99.2%) missing valuesMissing
DEP has 831 (9.8%) missing valuesMissing
DEPIF has 7876 (92.7%) missing valuesMissing
OTHPSY has 831 (9.8%) missing valuesMissing
OTHPSYIF has 8476 (99.7%) missing valuesMissing
DOWNS has 832 (9.8%) missing valuesMissing
DOWNSIF has 8498 (> 99.9%) missing valuesMissing
PARK has 868 (10.2%) missing valuesMissing
STROKE has 4511 (53.1%) missing valuesMissing
STROKIF has 8458 (99.5%) missing valuesMissing
HYCEPH has 832 (9.8%) missing valuesMissing
HYCEPHIF has 8495 (> 99.9%) missing valuesMissing
BRNINJ has 832 (9.8%) missing valuesMissing
BRNINJIF has 8462 (99.6%) missing valuesMissing
NEOP has 832 (9.8%) missing valuesMissing
NEOPIF has 8494 (99.9%) missing valuesMissing
COGOTH has 832 (9.8%) missing valuesMissing
COGOTHIF has 8347 (98.2%) missing valuesMissing
COGOTH2 has 1617 (19.0%) missing valuesMissing
COGOTH2F has 8480 (99.8%) missing valuesMissing
COGOTH3 has 1618 (19.0%) missing valuesMissing
COGOTH3F has 8495 (> 99.9%) missing valuesMissing
hiv has 4236 (49.8%) missing valuesMissing
dxmethod has 4855 (57.1%) missing valuesMissing
amndem has 7937 (93.4%) missing valuesMissing
pca has 7937 (93.4%) missing valuesMissing
ppasyn has 7937 (93.4%) missing valuesMissing
ppasynt has 8493 (99.9%) missing valuesMissing
ftdsyn has 7937 (93.4%) missing valuesMissing
lbdsyn has 7937 (93.4%) missing valuesMissing
namndem has 7937 (93.4%) missing valuesMissing
amylpet has 4856 (57.1%) missing valuesMissing
amylcsf has 4856 (57.1%) missing valuesMissing
fdgad has 4856 (57.1%) missing valuesMissing
hippatr has 4856 (57.1%) missing valuesMissing
taupetad has 4856 (57.1%) missing valuesMissing
csftau has 4856 (57.1%) missing valuesMissing
fdgftld has 4856 (57.1%) missing valuesMissing
tpetftld has 4856 (57.1%) missing valuesMissing
mrftld has 4856 (57.1%) missing valuesMissing
datscan has 4856 (57.1%) missing valuesMissing
othbiom has 4856 (57.1%) missing valuesMissing
imaglinf has 4856 (57.1%) missing valuesMissing
imaglac has 4856 (57.1%) missing valuesMissing
imagmach has 4856 (57.1%) missing valuesMissing
imagmich has 4856 (57.1%) missing valuesMissing
imagmwmh has 4856 (57.1%) missing valuesMissing
imagewmh has 4856 (57.1%) missing valuesMissing
admut has 4856 (57.1%) missing valuesMissing
ftldmut has 4856 (57.1%) missing valuesMissing
othmut has 4856 (57.1%) missing valuesMissing
alzdis has 4855 (57.1%) missing valuesMissing
alzdisif has 7930 (93.3%) missing valuesMissing
lbdis has 4855 (57.1%) missing valuesMissing
lbdif has 8484 (99.8%) missing valuesMissing
msa has 4855 (57.1%) missing valuesMissing
msaif has 8499 (100.0%) missing valuesMissing
ftldmo has 4855 (57.1%) missing valuesMissing
ftldmoif has 8499 (100.0%) missing valuesMissing
ftldnos has 4855 (57.1%) missing valuesMissing
ftldnoif has 8494 (99.9%) missing valuesMissing
ftldsubt has 8492 (99.9%) missing valuesMissing
cvd has 4855 (57.1%) missing valuesMissing
cvdif has 8463 (99.6%) missing valuesMissing
prevstk has 8436 (99.3%) missing valuesMissing
strokdec has 8441 (99.3%) missing valuesMissing
stkimag has 8441 (99.3%) missing valuesMissing
infnetw has 8436 (99.3%) missing valuesMissing
infwmh has 8436 (99.3%) missing valuesMissing
esstrem has 4855 (57.1%) missing valuesMissing
esstreif has 8437 (99.3%) missing valuesMissing
brnincte has 8479 (99.8%) missing valuesMissing
epilep has 4855 (57.1%) missing valuesMissing
epilepif has 8489 (99.9%) missing valuesMissing
neopstat has 8496 (> 99.9%) missing valuesMissing
hivif has 8499 (100.0%) missing valuesMissing
othcog has 4855 (57.1%) missing valuesMissing
othcogif has 8482 (99.8%) missing valuesMissing
deptreat has 8044 (94.6%) missing valuesMissing
bipoldx has 4855 (57.1%) missing valuesMissing
bipoldif has 8494 (99.9%) missing valuesMissing
schizop has 4855 (57.1%) missing valuesMissing
schizoif has 8497 (> 99.9%) missing valuesMissing
anxiet has 4855 (57.1%) missing valuesMissing
anxietif has 8456 (99.5%) missing valuesMissing
delir has 4855 (57.1%) missing valuesMissing
delirif has 8499 (100.0%) missing valuesMissing
ptsddx has 4855 (57.1%) missing valuesMissing
ptsddxif has 8498 (> 99.9%) missing valuesMissing
alcabuse has 8475 (99.7%) missing valuesMissing
impsub has 4855 (57.1%) missing valuesMissing
impsubif has 8494 (99.9%) missing valuesMissing
schizoif is uniformly distributedUniform
OASIS_session_label has unique valuesUnique
HUNTIF is an unsupported type, check if it needs cleaning or further analysisUnsupported
PRIONIF is an unsupported type, check if it needs cleaning or further analysisUnsupported
msaif is an unsupported type, check if it needs cleaning or further analysisUnsupported
ftldmoif is an unsupported type, check if it needs cleaning or further analysisUnsupported
hivif is an unsupported type, check if it needs cleaning or further analysisUnsupported
delirif is an unsupported type, check if it needs cleaning or further analysisUnsupported
days_to_visit has 1323 (15.6%) zerosZeros

Reproduction

Analysis started2023-10-17 18:16:30.069022
Analysis finished2023-10-17 18:17:48.136840
Duration1 minute and 18.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct1340
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size66.5 KiB
2023-10-17T23:47:48.967506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters67992
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)1.4%

Sample

1st rowOAS30001
2nd rowOAS30001
3rd rowOAS30001
4th rowOAS30001
5th rowOAS30001
ValueCountFrequency (%)
oas30446 32
 
0.4%
oas30936 31
 
0.4%
oas30393 30
 
0.4%
oas30675 30
 
0.4%
oas31155 28
 
0.3%
oas30194 28
 
0.3%
oas30314 26
 
0.3%
oas31160 25
 
0.3%
oas31100 25
 
0.3%
oas30825 24
 
0.3%
Other values (1330) 8220
96.7%
2023-10-17T23:47:50.352912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 11170
16.4%
0 9576
14.1%
O 8499
12.5%
A 8499
12.5%
S 8499
12.5%
1 4868
7.2%
2 2612
 
3.8%
7 2541
 
3.7%
4 2481
 
3.6%
5 2460
 
3.6%
Other values (3) 6787
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42495
62.5%
Uppercase Letter 25497
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 11170
26.3%
0 9576
22.5%
1 4868
11.5%
2 2612
 
6.1%
7 2541
 
6.0%
4 2481
 
5.8%
5 2460
 
5.8%
8 2317
 
5.5%
6 2290
 
5.4%
9 2180
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
O 8499
33.3%
A 8499
33.3%
S 8499
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 42495
62.5%
Latin 25497
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
3 11170
26.3%
0 9576
22.5%
1 4868
11.5%
2 2612
 
6.1%
7 2541
 
6.0%
4 2481
 
5.8%
5 2460
 
5.8%
8 2317
 
5.5%
6 2290
 
5.4%
9 2180
 
5.1%
Latin
ValueCountFrequency (%)
O 8499
33.3%
A 8499
33.3%
S 8499
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 11170
16.4%
0 9576
14.1%
O 8499
12.5%
A 8499
12.5%
S 8499
12.5%
1 4868
7.2%
2 2612
 
3.8%
7 2541
 
3.7%
4 2481
 
3.6%
5 2460
 
3.6%
Other values (3) 6787
10.0%

OASIS_session_label
Text

UNIQUE 

Distinct8499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size66.5 KiB
2023-10-17T23:47:50.966399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length21
Median length20
Mean length20.002353
Min length20

Characters and Unicode

Total characters170000
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8499 ?
Unique (%)100.0%

Sample

1st rowOAS30001_UDSd1_d0000
2nd rowOAS30001_UDSd1_d0339
3rd rowOAS30001_UDSd1_d0722
4th rowOAS30001_UDSd1_d1106
5th rowOAS30001_UDSd1_d1456
ValueCountFrequency (%)
oas30001_udsd1_d0000 1
 
< 0.1%
oas30002_udsd1_d1169 1
 
< 0.1%
oas30001_udsd1_d1106 1
 
< 0.1%
oas30001_udsd1_d1456 1
 
< 0.1%
oas30001_udsd1_d1894 1
 
< 0.1%
oas30001_udsd1_d2181 1
 
< 0.1%
oas30001_udsd1_d2699 1
 
< 0.1%
oas30001_udsd1_d3025 1
 
< 0.1%
oas30001_udsd1_d3332 1
 
< 0.1%
oas30001_udsd1_d3675 1
 
< 0.1%
Other values (8489) 8489
99.9%
2023-10-17T23:47:51.919060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18507
10.9%
1 17616
10.4%
S 16998
10.0%
_ 16998
10.0%
d 16998
10.0%
3 14470
8.5%
O 8499
 
5.0%
A 8499
 
5.0%
D 8499
 
5.0%
U 8499
 
5.0%
Other values (7) 34417
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 85010
50.0%
Uppercase Letter 50994
30.0%
Connector Punctuation 16998
 
10.0%
Lowercase Letter 16998
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18507
21.8%
1 17616
20.7%
3 14470
17.0%
2 5946
 
7.0%
4 5496
 
6.5%
7 4932
 
5.8%
5 4930
 
5.8%
8 4531
 
5.3%
6 4421
 
5.2%
9 4161
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
S 16998
33.3%
O 8499
16.7%
A 8499
16.7%
D 8499
16.7%
U 8499
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 16998
100.0%
Lowercase Letter
ValueCountFrequency (%)
d 16998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 102008
60.0%
Latin 67992
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18507
18.1%
1 17616
17.3%
_ 16998
16.7%
3 14470
14.2%
2 5946
 
5.8%
4 5496
 
5.4%
7 4932
 
4.8%
5 4930
 
4.8%
8 4531
 
4.4%
6 4421
 
4.3%
Latin
ValueCountFrequency (%)
S 16998
25.0%
d 16998
25.0%
O 8499
12.5%
A 8499
12.5%
D 8499
12.5%
U 8499
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18507
10.9%
1 17616
10.4%
S 16998
10.0%
_ 16998
10.0%
d 16998
10.0%
3 14470
8.5%
O 8499
 
5.0%
A 8499
 
5.0%
D 8499
 
5.0%
U 8499
 
5.0%
Other values (7) 34417
20.2%

days_to_visit
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3398
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2048.002
Minimum0
Maximum12334
Zeros1323
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size66.5 KiB
2023-10-17T23:47:52.434439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1479.5
median1522
Q33098
95-th percentile5626.1
Maximum12334
Range12334
Interquartile range (IQR)2618.5

Descriptive statistics

Standard deviation1901.4277
Coefficient of variation (CV)0.92843058
Kurtosis1.7751999
Mean2048.002
Median Absolute Deviation (MAD)1146
Skewness1.245241
Sum17405969
Variance3615427.3
MonotonicityNot monotonic
2023-10-17T23:47:52.970498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1323
 
15.6%
371 26
 
0.3%
364 19
 
0.2%
385 18
 
0.2%
378 17
 
0.2%
1099 14
 
0.2%
357 13
 
0.2%
406 13
 
0.2%
363 13
 
0.2%
728 13
 
0.2%
Other values (3388) 7030
82.7%
ValueCountFrequency (%)
0 1323
15.6%
1 4
 
< 0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
10 1
 
< 0.1%
15 1
 
< 0.1%
31 1
 
< 0.1%
46 1
 
< 0.1%
173 1
 
< 0.1%
ValueCountFrequency (%)
12334 1
< 0.1%
11849 1
< 0.1%
11723 1
< 0.1%
11639 1
< 0.1%
11504 1
< 0.1%
11493 1
< 0.1%
11303 1
< 0.1%
11066 1
< 0.1%
10928 1
< 0.1%
10711 1
< 0.1%

age at visit
Real number (ℝ)

HIGH CORRELATION 

Distinct3131
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.385815
Minimum42.5
Maximum100.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size66.5 KiB
2023-10-17T23:47:53.506279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum42.5
5-th percentile59.519
Q169.33
median74.45
Q379.91
95-th percentile88.14
Maximum100.55
Range58.05
Interquartile range (IQR)10.58

Descriptive statistics

Standard deviation8.4891437
Coefficient of variation (CV)0.11412315
Kurtosis0.48869692
Mean74.385815
Median Absolute Deviation (MAD)5.28
Skewness-0.27266324
Sum632205.04
Variance72.065561
MonotonicityNot monotonic
2023-10-17T23:47:54.007613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.04 12
 
0.1%
70.75 12
 
0.1%
73.59 12
 
0.1%
73.78 11
 
0.1%
68.75 11
 
0.1%
67.53 11
 
0.1%
74.07 11
 
0.1%
69.85 11
 
0.1%
69.44 11
 
0.1%
69.42 10
 
0.1%
Other values (3121) 8387
98.7%
ValueCountFrequency (%)
42.5 1
< 0.1%
43.24 1
< 0.1%
43.5 1
< 0.1%
45.22 1
< 0.1%
45.24 1
< 0.1%
45.3 1
< 0.1%
45.52 1
< 0.1%
45.61 1
< 0.1%
45.66 2
< 0.1%
45.7 1
< 0.1%
ValueCountFrequency (%)
100.55 1
< 0.1%
99.24 1
< 0.1%
98.95 1
< 0.1%
98.9 1
< 0.1%
98.73 1
< 0.1%
98.69 1
< 0.1%
98.34 1
< 0.1%
98.27 1
< 0.1%
97.98 1
< 0.1%
97.85 1
< 0.1%

WHODIDDX
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing4477
Missing (%)52.7%
Memory size66.5 KiB
1.0
4019 
0.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12066
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4019
47.3%
0.0 3
 
< 0.1%
(Missing) 4477
52.7%

Length

2023-10-17T23:47:54.477297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:47:54.948936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4019
99.9%
0.0 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 4025
33.4%
. 4022
33.3%
1 4019
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8044
66.7%
Other Punctuation 4022
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4025
50.0%
1 4019
50.0%
Other Punctuation
ValueCountFrequency (%)
. 4022
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12066
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4025
33.4%
. 4022
33.3%
1 4019
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4025
33.4%
. 4022
33.3%
1 4019
33.3%

NORMCOG
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing837
Missing (%)9.8%
Memory size66.5 KiB
1.0
5799 
0.0
1863 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22986
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5799
68.2%
0.0 1863
 
21.9%
(Missing) 837
 
9.8%

Length

2023-10-17T23:47:55.319684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:47:55.724598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5799
75.7%
0.0 1863
 
24.3%

Most occurring characters

ValueCountFrequency (%)
0 9525
41.4%
. 7662
33.3%
1 5799
25.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15324
66.7%
Other Punctuation 7662
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9525
62.2%
1 5799
37.8%
Other Punctuation
ValueCountFrequency (%)
. 7662
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22986
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9525
41.4%
. 7662
33.3%
1 5799
25.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22986
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9525
41.4%
. 7662
33.3%
1 5799
25.2%

DEMENTED
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.2%
Missing6623
Missing (%)77.9%
Memory size66.5 KiB
1.0
1378 
0.0
497 
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5628
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 1378
 
16.2%
0.0 497
 
5.8%
2.0 1
 
< 0.1%
(Missing) 6623
77.9%

Length

2023-10-17T23:47:56.093987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:47:56.524603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1378
73.5%
0.0 497
 
26.5%
2.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2373
42.2%
. 1876
33.3%
1 1378
24.5%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3752
66.7%
Other Punctuation 1876
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2373
63.2%
1 1378
36.7%
2 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1876
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5628
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2373
42.2%
. 1876
33.3%
1 1378
24.5%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2373
42.2%
. 1876
33.3%
1 1378
24.5%
2 1
 
< 0.1%

MCIAMEM
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.4%
Missing8020
Missing (%)94.4%
Memory size66.5 KiB
0.0
396 
1.0
83 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1437
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 396
 
4.7%
1.0 83
 
1.0%
(Missing) 8020
94.4%

Length

2023-10-17T23:47:56.931618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:47:57.327984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 396
82.7%
1.0 83
 
17.3%

Most occurring characters

ValueCountFrequency (%)
0 875
60.9%
. 479
33.3%
1 83
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 958
66.7%
Other Punctuation 479
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 875
91.3%
1 83
 
8.7%
Other Punctuation
ValueCountFrequency (%)
. 479
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1437
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 875
60.9%
. 479
33.3%
1 83
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 875
60.9%
. 479
33.3%
1 83
 
5.8%

MCIAPLUS
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.4%
Missing8021
Missing (%)94.4%
Memory size66.5 KiB
0.0
363 
1.0
115 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1434
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 363
 
4.3%
1.0 115
 
1.4%
(Missing) 8021
94.4%

Length

2023-10-17T23:47:57.689652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:47:58.096451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 363
75.9%
1.0 115
 
24.1%

Most occurring characters

ValueCountFrequency (%)
0 841
58.6%
. 478
33.3%
1 115
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 956
66.7%
Other Punctuation 478
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 841
88.0%
1 115
 
12.0%
Other Punctuation
ValueCountFrequency (%)
. 478
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1434
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 841
58.6%
. 478
33.3%
1 115
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 841
58.6%
. 478
33.3%
1 115
 
8.0%

MCIAPLAN
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)1.9%
Missing8391
Missing (%)98.7%
Memory size66.5 KiB
0.0
78 
1.0
30 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters324
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 78
 
0.9%
1.0 30
 
0.4%
(Missing) 8391
98.7%

Length

2023-10-17T23:47:58.509746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:47:58.950644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 78
72.2%
1.0 30
 
27.8%

Most occurring characters

ValueCountFrequency (%)
0 186
57.4%
. 108
33.3%
1 30
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 216
66.7%
Other Punctuation 108
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 186
86.1%
1 30
 
13.9%
Other Punctuation
ValueCountFrequency (%)
. 108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 324
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 186
57.4%
. 108
33.3%
1 30
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 186
57.4%
. 108
33.3%
1 30
 
9.3%

MCIAPATT
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)1.8%
Missing8390
Missing (%)98.7%
Memory size66.5 KiB
0.0
81 
1.0
28 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters327
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 81
 
1.0%
1.0 28
 
0.3%
(Missing) 8390
98.7%

Length

2023-10-17T23:47:59.276881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:47:59.714098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 81
74.3%
1.0 28
 
25.7%

Most occurring characters

ValueCountFrequency (%)
0 190
58.1%
. 109
33.3%
1 28
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 218
66.7%
Other Punctuation 109
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 190
87.2%
1 28
 
12.8%
Other Punctuation
ValueCountFrequency (%)
. 109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 327
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 190
58.1%
. 109
33.3%
1 28
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 190
58.1%
. 109
33.3%
1 28
 
8.6%

MCIAPEX
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)1.8%
Missing8386
Missing (%)98.7%
Memory size66.5 KiB
1.0
91 
0.0
22 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters339
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 91
 
1.1%
0.0 22
 
0.3%
(Missing) 8386
98.7%

Length

2023-10-17T23:48:00.106713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:00.544144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 91
80.5%
0.0 22
 
19.5%

Most occurring characters

ValueCountFrequency (%)
0 135
39.8%
. 113
33.3%
1 91
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 226
66.7%
Other Punctuation 113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 135
59.7%
1 91
40.3%
Other Punctuation
ValueCountFrequency (%)
. 113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 135
39.8%
. 113
33.3%
1 91
26.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 135
39.8%
. 113
33.3%
1 91
26.8%

MCIAPVIS
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)1.9%
Missing8391
Missing (%)98.7%
Memory size66.5 KiB
0.0
97 
1.0
11 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters324
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 97
 
1.1%
1.0 11
 
0.1%
(Missing) 8391
98.7%

Length

2023-10-17T23:48:00.867677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:01.314121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 97
89.8%
1.0 11
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 205
63.3%
. 108
33.3%
1 11
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 216
66.7%
Other Punctuation 108
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 205
94.9%
1 11
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 324
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 205
63.3%
. 108
33.3%
1 11
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 205
63.3%
. 108
33.3%
1 11
 
3.4%

MCINON1
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.4%
Missing8015
Missing (%)94.3%
Memory size66.5 KiB
0.0
469 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1452
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 469
 
5.5%
1.0 15
 
0.2%
(Missing) 8015
94.3%

Length

2023-10-17T23:48:01.707774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:02.114784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 469
96.9%
1.0 15
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 953
65.6%
. 484
33.3%
1 15
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 968
66.7%
Other Punctuation 484
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953
98.5%
1 15
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1452
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953
65.6%
. 484
33.3%
1 15
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953
65.6%
. 484
33.3%
1 15
 
1.0%

MCIN1LAN
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)11.8%
Missing8482
Missing (%)99.8%
Memory size66.5 KiB
0.0
16 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters51
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.9%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 16
 
0.2%
1.0 1
 
< 0.1%
(Missing) 8482
99.8%

Length

2023-10-17T23:48:02.428303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:02.877409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 16
94.1%
1.0 1
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 33
64.7%
. 17
33.3%
1 1
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34
66.7%
Other Punctuation 17
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33
97.1%
1 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33
64.7%
. 17
33.3%
1 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33
64.7%
. 17
33.3%
1 1
 
2.0%

MCIN1ATT
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)12.5%
Missing8483
Missing (%)99.8%
Memory size66.5 KiB
0.0
13 
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters48
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 13
 
0.2%
1.0 3
 
< 0.1%
(Missing) 8483
99.8%

Length

2023-10-17T23:48:03.068409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:03.199958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 13
81.2%
1.0 3
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0 29
60.4%
. 16
33.3%
1 3
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32
66.7%
Other Punctuation 16
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29
90.6%
1 3
 
9.4%
Other Punctuation
ValueCountFrequency (%)
. 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29
60.4%
. 16
33.3%
1 3
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29
60.4%
. 16
33.3%
1 3
 
6.2%

MCIN1EX
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)10.5%
Missing8480
Missing (%)99.8%
Memory size66.5 KiB
1.0
10 
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10
 
0.1%
0.0 9
 
0.1%
(Missing) 8480
99.8%

Length

2023-10-17T23:48:03.319931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:03.446540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10
52.6%
0.0 9
47.4%

Most occurring characters

ValueCountFrequency (%)
0 28
49.1%
. 19
33.3%
1 10
 
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38
66.7%
Other Punctuation 19
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
73.7%
1 10
 
26.3%
Other Punctuation
ValueCountFrequency (%)
. 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28
49.1%
. 19
33.3%
1 10
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
49.1%
. 19
33.3%
1 10
 
17.5%

MCIN1VIS
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)6.2%
Missing8483
Missing (%)99.8%
Memory size66.5 KiB
0.0
16 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters48
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 16
 
0.2%
(Missing) 8483
99.8%

Length

2023-10-17T23:48:03.587273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:03.784383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 16
100.0%

Most occurring characters

ValueCountFrequency (%)
0 32
66.7%
. 16
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32
66.7%
Other Punctuation 16
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32
100.0%
Other Punctuation
ValueCountFrequency (%)
. 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32
66.7%
. 16
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32
66.7%
. 16
33.3%

MCINON2
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.4%
Missing8018
Missing (%)94.3%
Memory size66.5 KiB
0.0
470 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1443
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 470
 
5.5%
1.0 11
 
0.1%
(Missing) 8018
94.3%

Length

2023-10-17T23:48:03.910431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:04.039729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 470
97.7%
1.0 11
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 951
65.9%
. 481
33.3%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 962
66.7%
Other Punctuation 481
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 951
98.9%
1 11
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 481
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1443
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 951
65.9%
. 481
33.3%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 951
65.9%
. 481
33.3%
1 11
 
0.8%

MCIN2LAN
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)15.4%
Missing8486
Missing (%)99.8%
Memory size66.5 KiB
0.0
10 
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 10
 
0.1%
1.0 3
 
< 0.1%
(Missing) 8486
99.8%

Length

2023-10-17T23:48:04.158336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:04.338560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10
76.9%
1.0 3
 
23.1%

Most occurring characters

ValueCountFrequency (%)
0 23
59.0%
. 13
33.3%
1 3
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26
66.7%
Other Punctuation 13
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23
88.5%
1 3
 
11.5%
Other Punctuation
ValueCountFrequency (%)
. 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23
59.0%
. 13
33.3%
1 3
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23
59.0%
. 13
33.3%
1 3
 
7.7%

MCIN2ATT
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)15.4%
Missing8486
Missing (%)99.8%
Memory size66.5 KiB
1.0
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 7
 
0.1%
0.0 6
 
0.1%
(Missing) 8486
99.8%

Length

2023-10-17T23:48:04.458223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:04.634434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7
53.8%
0.0 6
46.2%

Most occurring characters

ValueCountFrequency (%)
0 19
48.7%
. 13
33.3%
1 7
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26
66.7%
Other Punctuation 13
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19
73.1%
1 7
 
26.9%
Other Punctuation
ValueCountFrequency (%)
. 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19
48.7%
. 13
33.3%
1 7
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19
48.7%
. 13
33.3%
1 7
 
17.9%

MCIN2EX
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)10.5%
Missing8480
Missing (%)99.8%
Memory size66.5 KiB
1.0
10 
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10
 
0.1%
0.0 9
 
0.1%
(Missing) 8480
99.8%

Length

2023-10-17T23:48:04.752408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:04.936544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10
52.6%
0.0 9
47.4%

Most occurring characters

ValueCountFrequency (%)
0 28
49.1%
. 19
33.3%
1 10
 
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38
66.7%
Other Punctuation 19
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
73.7%
1 10
 
26.3%
Other Punctuation
ValueCountFrequency (%)
. 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28
49.1%
. 19
33.3%
1 10
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
49.1%
. 19
33.3%
1 10
 
17.5%

MCIN2VIS
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)7.7%
Missing8486
Missing (%)99.8%
Memory size66.5 KiB
0.0
13 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters39
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 13
 
0.2%
(Missing) 8486
99.8%

Length

2023-10-17T23:48:05.104641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:05.286471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 13
100.0%

Most occurring characters

ValueCountFrequency (%)
0 26
66.7%
. 13
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26
66.7%
Other Punctuation 13
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26
100.0%
Other Punctuation
ValueCountFrequency (%)
. 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26
66.7%
. 13
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26
66.7%
. 13
33.3%

IMPNOMCI
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.6%
Missing8015
Missing (%)94.3%
Memory size66.5 KiB
1.0
256 
0.0
226 
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1452
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 256
 
3.0%
0.0 226
 
2.7%
2.0 2
 
< 0.1%
(Missing) 8015
94.3%

Length

2023-10-17T23:48:05.424403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:05.596481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 256
52.9%
0.0 226
46.7%
2.0 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 710
48.9%
. 484
33.3%
1 256
 
17.6%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 968
66.7%
Other Punctuation 484
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 710
73.3%
1 256
 
26.4%
2 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1452
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 710
48.9%
. 484
33.3%
1 256
 
17.6%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 710
48.9%
. 484
33.3%
1 256
 
17.6%
2 2
 
0.1%

PROBAD
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing7368
Missing (%)86.7%
Memory size66.5 KiB
1.0
685 
0.0
446 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3393
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 685
 
8.1%
0.0 446
 
5.2%
(Missing) 7368
86.7%

Length

2023-10-17T23:48:05.726877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:05.874758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 685
60.6%
0.0 446
39.4%

Most occurring characters

ValueCountFrequency (%)
0 1577
46.5%
. 1131
33.3%
1 685
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2262
66.7%
Other Punctuation 1131
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1577
69.7%
1 685
30.3%
Other Punctuation
ValueCountFrequency (%)
. 1131
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1577
46.5%
. 1131
33.3%
1 685
20.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1577
46.5%
. 1131
33.3%
1 685
20.2%

PROBADIF
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)0.4%
Missing7832
Missing (%)92.2%
Memory size66.5 KiB
1.0
656 
2.0
 
9
0.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2001
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 656
 
7.7%
2.0 9
 
0.1%
0.0 2
 
< 0.1%
(Missing) 7832
92.2%

Length

2023-10-17T23:48:05.974602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:06.139033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 656
98.4%
2.0 9
 
1.3%
0.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 669
33.4%
. 667
33.3%
1 656
32.8%
2 9
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1334
66.7%
Other Punctuation 667
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 669
50.1%
1 656
49.2%
2 9
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 669
33.4%
. 667
33.3%
1 656
32.8%
2 9
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 669
33.4%
. 667
33.3%
1 656
32.8%
2 9
 
0.4%

POSSAD
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.4%
Missing8041
Missing (%)94.6%
Memory size66.5 KiB
0.0
259 
1.0
199 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1374
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 259
 
3.0%
1.0 199
 
2.3%
(Missing) 8041
94.6%

Length

2023-10-17T23:48:06.274675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:06.459631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 259
56.6%
1.0 199
43.4%

Most occurring characters

ValueCountFrequency (%)
0 717
52.2%
. 458
33.3%
1 199
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 916
66.7%
Other Punctuation 458
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 717
78.3%
1 199
 
21.7%
Other Punctuation
ValueCountFrequency (%)
. 458
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1374
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 717
52.2%
. 458
33.3%
1 199
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 717
52.2%
. 458
33.3%
1 199
 
14.5%

POSSADIF
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)1.0%
Missing8304
Missing (%)97.7%
Memory size66.5 KiB
1.0
156 
2.0
39 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters585
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 156
 
1.8%
2.0 39
 
0.5%
(Missing) 8304
97.7%

Length

2023-10-17T23:48:06.629138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:06.790061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 156
80.0%
2.0 39
 
20.0%

Most occurring characters

ValueCountFrequency (%)
. 195
33.3%
0 195
33.3%
1 156
26.7%
2 39
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 390
66.7%
Other Punctuation 195
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 195
50.0%
1 156
40.0%
2 39
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 585
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 195
33.3%
0 195
33.3%
1 156
26.7%
2 39
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 195
33.3%
0 195
33.3%
1 156
26.7%
2 39
 
6.7%

DLB
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing7367
Missing (%)86.7%
Memory size66.5 KiB
0.0
1102 
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3396
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1102
 
13.0%
1.0 30
 
0.4%
(Missing) 7367
86.7%

Length

2023-10-17T23:48:06.904402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:07.054788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1102
97.3%
1.0 30
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 2234
65.8%
. 1132
33.3%
1 30
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2264
66.7%
Other Punctuation 1132
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2234
98.7%
1 30
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 1132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3396
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2234
65.8%
. 1132
33.3%
1 30
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2234
65.8%
. 1132
33.3%
1 30
 
0.9%

DLBIF
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)7.1%
Missing8471
Missing (%)99.7%
Memory size66.5 KiB
1.0
16 
2.0
12 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters84
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 16
 
0.2%
2.0 12
 
0.1%
(Missing) 8471
99.7%

Length

2023-10-17T23:48:07.177206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:07.328790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 16
57.1%
2.0 12
42.9%

Most occurring characters

ValueCountFrequency (%)
. 28
33.3%
0 28
33.3%
1 16
19.0%
2 12
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56
66.7%
Other Punctuation 28
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
50.0%
1 16
28.6%
2 12
21.4%
Other Punctuation
ValueCountFrequency (%)
. 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 28
33.3%
0 28
33.3%
1 16
19.0%
2 12
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 28
33.3%
0 28
33.3%
1 16
19.0%
2 12
14.3%

VASC
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing7367
Missing (%)86.7%
Memory size66.5 KiB
0.0
1116 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3396
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1116
 
13.1%
1.0 16
 
0.2%
(Missing) 7367
86.7%

Length

2023-10-17T23:48:07.466969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:07.683832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1116
98.6%
1.0 16
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 2248
66.2%
. 1132
33.3%
1 16
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2264
66.7%
Other Punctuation 1132
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2248
99.3%
1 16
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 1132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3396
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2248
66.2%
. 1132
33.3%
1 16
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2248
66.2%
. 1132
33.3%
1 16
 
0.5%

VASCIF
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)12.5%
Missing8483
Missing (%)99.8%
Memory size66.5 KiB
1.0
10 
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters48
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10
 
0.1%
2.0 6
 
0.1%
(Missing) 8483
99.8%

Length

2023-10-17T23:48:07.816342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:08.007283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10
62.5%
2.0 6
37.5%

Most occurring characters

ValueCountFrequency (%)
. 16
33.3%
0 16
33.3%
1 10
20.8%
2 6
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32
66.7%
Other Punctuation 16
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16
50.0%
1 10
31.2%
2 6
 
18.8%
Other Punctuation
ValueCountFrequency (%)
. 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 16
33.3%
0 16
33.3%
1 10
20.8%
2 6
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 16
33.3%
0 16
33.3%
1 10
20.8%
2 6
 
12.5%

VASCPS
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing7593
Missing (%)89.3%
Memory size66.5 KiB
0.0
895 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2718
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 895
 
10.5%
1.0 11
 
0.1%
(Missing) 7593
89.3%

Length

2023-10-17T23:48:08.128635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:08.256435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 895
98.8%
1.0 11
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1801
66.3%
. 906
33.3%
1 11
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1812
66.7%
Other Punctuation 906
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1801
99.4%
1 11
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 906
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2718
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1801
66.3%
. 906
33.3%
1 11
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1801
66.3%
. 906
33.3%
1 11
 
0.4%

VASCPSIF
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)18.2%
Missing8488
Missing (%)99.9%
Memory size66.5 KiB
2.0
10 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)9.1%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 10
 
0.1%
1.0 1
 
< 0.1%
(Missing) 8488
99.9%

Length

2023-10-17T23:48:08.366500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:08.533721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 10
90.9%
1.0 1
 
9.1%

Most occurring characters

ValueCountFrequency (%)
. 11
33.3%
0 11
33.3%
2 10
30.3%
1 1
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22
66.7%
Other Punctuation 11
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11
50.0%
2 10
45.5%
1 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 11
33.3%
0 11
33.3%
2 10
30.3%
1 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 11
33.3%
0 11
33.3%
2 10
30.3%
1 1
 
3.0%

ALCDEM
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing3723
Missing (%)43.8%
Memory size66.5 KiB
0.0
4747 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14328
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4747
55.9%
1.0 29
 
0.3%
(Missing) 3723
43.8%

Length

2023-10-17T23:48:08.639694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:08.780186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4747
99.4%
1.0 29
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 9523
66.5%
. 4776
33.3%
1 29
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9552
66.7%
Other Punctuation 4776
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9523
99.7%
1 29
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 4776
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14328
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9523
66.5%
. 4776
33.3%
1 29
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9523
66.5%
. 4776
33.3%
1 29
 
0.2%

ALCDEMIF
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)11.1%
Missing8472
Missing (%)99.7%
Memory size66.5 KiB
1.0
15 
2.0
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 15
 
0.2%
2.0 9
 
0.1%
3.0 3
 
< 0.1%
(Missing) 8472
99.7%

Length

2023-10-17T23:48:08.927951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:09.096436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 15
55.6%
2.0 9
33.3%
3.0 3
 
11.1%

Most occurring characters

ValueCountFrequency (%)
. 27
33.3%
0 27
33.3%
1 15
18.5%
2 9
 
11.1%
3 3
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54
66.7%
Other Punctuation 27
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27
50.0%
1 15
27.8%
2 9
 
16.7%
3 3
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 81
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 27
33.3%
0 27
33.3%
1 15
18.5%
2 9
 
11.1%
3 3
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 27
33.3%
0 27
33.3%
1 15
18.5%
2 9
 
11.1%
3 3
 
3.7%

DEMUN
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing7367
Missing (%)86.7%
Memory size66.5 KiB
0.0
1089 
1.0
 
43

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3396
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1089
 
12.8%
1.0 43
 
0.5%
(Missing) 7367
86.7%

Length

2023-10-17T23:48:09.272905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:09.413801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1089
96.2%
1.0 43
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 2221
65.4%
. 1132
33.3%
1 43
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2264
66.7%
Other Punctuation 1132
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2221
98.1%
1 43
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 1132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3396
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2221
65.4%
. 1132
33.3%
1 43
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2221
65.4%
. 1132
33.3%
1 43
 
1.3%

DEMUNIF
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)4.8%
Missing8457
Missing (%)99.5%
Memory size66.5 KiB
1.0
38 
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters126
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 38
 
0.4%
2.0 4
 
< 0.1%
(Missing) 8457
99.5%

Length

2023-10-17T23:48:09.544817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:10.220123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 38
90.5%
2.0 4
 
9.5%

Most occurring characters

ValueCountFrequency (%)
. 42
33.3%
0 42
33.3%
1 38
30.2%
2 4
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84
66.7%
Other Punctuation 42
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42
50.0%
1 38
45.2%
2 4
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 126
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 42
33.3%
0 42
33.3%
1 38
30.2%
2 4
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 42
33.3%
0 42
33.3%
1 38
30.2%
2 4
 
3.2%

FTD
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing7366
Missing (%)86.7%
Memory size66.5 KiB
0.0
1120 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3399
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1120
 
13.2%
1.0 13
 
0.2%
(Missing) 7366
86.7%

Length

2023-10-17T23:48:10.348939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:10.486341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1120
98.9%
1.0 13
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 2253
66.3%
. 1133
33.3%
1 13
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2266
66.7%
Other Punctuation 1133
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2253
99.4%
1 13
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 1133
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3399
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2253
66.3%
. 1133
33.3%
1 13
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3399
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2253
66.3%
. 1133
33.3%
1 13
 
0.4%

FTDIF
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)15.4%
Missing8486
Missing (%)99.8%
Memory size66.5 KiB
1.0
12 
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)7.7%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 12
 
0.1%
0.0 1
 
< 0.1%
(Missing) 8486
99.8%

Length

2023-10-17T23:48:10.617077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:10.837281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12
92.3%
0.0 1
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 14
35.9%
. 13
33.3%
1 12
30.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26
66.7%
Other Punctuation 13
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14
53.8%
1 12
46.2%
Other Punctuation
ValueCountFrequency (%)
. 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14
35.9%
. 13
33.3%
1 12
30.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14
35.9%
. 13
33.3%
1 12
30.8%

PPAPH
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing7358
Missing (%)86.6%
Memory size66.5 KiB
0.0
1135 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3423
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1135
 
13.4%
1.0 6
 
0.1%
(Missing) 7358
86.6%

Length

2023-10-17T23:48:10.977794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:11.087559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1135
99.5%
1.0 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 2276
66.5%
. 1141
33.3%
1 6
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2282
66.7%
Other Punctuation 1141
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2276
99.7%
1 6
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 1141
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3423
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2276
66.5%
. 1141
33.3%
1 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3423
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2276
66.5%
. 1141
33.3%
1 6
 
0.2%

PPAPHIF
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)40.0%
Missing8494
Missing (%)99.9%
Memory size66.5 KiB
2.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 3
 
< 0.1%
1.0 2
 
< 0.1%
(Missing) 8494
99.9%

Length

2023-10-17T23:48:11.191652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:11.304584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 3
60.0%
1.0 2
40.0%

Most occurring characters

ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
1 2
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5
50.0%
2 3
30.0%
1 2
 
20.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
1 2
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
1 2
 
13.3%

PNAPH
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)33.3%
Missing8493
Missing (%)99.9%
Memory size66.5 KiB
0.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4
 
< 0.1%
1.0 2
 
< 0.1%
(Missing) 8493
99.9%

Length

2023-10-17T23:48:11.446533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:11.584375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4
66.7%
1.0 2
33.3%

Most occurring characters

ValueCountFrequency (%)
0 10
55.6%
. 6
33.3%
1 2
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12
66.7%
Other Punctuation 6
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10
83.3%
1 2
 
16.7%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10
55.6%
. 6
33.3%
1 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10
55.6%
. 6
33.3%
1 2
 
11.1%

SEMDEMAN
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing8494
Missing (%)99.9%
Memory size66.5 KiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5
 
0.1%
(Missing) 8494
99.9%

Length

2023-10-17T23:48:11.688184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:11.861128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10
66.7%
. 5
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10
66.7%
. 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10
66.7%
. 5
33.3%

SEMDEMAG
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)16.7%
Missing8493
Missing (%)99.9%
Memory size66.5 KiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6
 
0.1%
(Missing) 8493
99.9%

Length

2023-10-17T23:48:11.953648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:12.064532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6
100.0%

Most occurring characters

ValueCountFrequency (%)
0 12
66.7%
. 6
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12
66.7%
Other Punctuation 6
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12
66.7%
. 6
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12
66.7%
. 6
33.3%

PPAOTHR
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)33.3%
Missing8493
Missing (%)99.9%
Memory size66.5 KiB
0.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 4
 
< 0.1%
1.0 2
 
< 0.1%
(Missing) 8493
99.9%

Length

2023-10-17T23:48:12.163641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:12.304451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4
66.7%
1.0 2
33.3%

Most occurring characters

ValueCountFrequency (%)
0 10
55.6%
. 6
33.3%
1 2
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12
66.7%
Other Punctuation 6
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10
83.3%
1 2
 
16.7%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10
55.6%
. 6
33.3%
1 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10
55.6%
. 6
33.3%
1 2
 
11.1%

PSP
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing832
Missing (%)9.8%
Memory size66.5 KiB
0.0
7665 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23001
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7665
90.2%
1.0 2
 
< 0.1%
(Missing) 832
 
9.8%

Length

2023-10-17T23:48:12.416603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:12.556282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7665
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15332
66.7%
. 7667
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15334
66.7%
Other Punctuation 7667
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15332
> 99.9%
1 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 7667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15332
66.7%
. 7667
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15332
66.7%
. 7667
33.3%
1 2
 
< 0.1%

PSPIF
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing8497
Missing (%)> 99.9%
Memory size66.5 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
(Missing) 8497
> 99.9%

Length

2023-10-17T23:48:12.676969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:12.808900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

CORT
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing832
Missing (%)9.8%
Memory size66.5 KiB
0.0
7666 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23001
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7666
90.2%
1.0 1
 
< 0.1%
(Missing) 832
 
9.8%

Length

2023-10-17T23:48:12.954825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:13.149040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7666
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15333
66.7%
. 7667
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15334
66.7%
Other Punctuation 7667
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15333
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 7667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15333
66.7%
. 7667
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15333
66.7%
. 7667
33.3%
1 1
 
< 0.1%

CORTIF
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing8498
Missing (%)> 99.9%
Memory size66.5 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 8498
> 99.9%

Length

2023-10-17T23:48:13.266916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:13.391444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

HUNT
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing832
Missing (%)9.8%
Memory size66.5 KiB
0.0
7667 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23001
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7667
90.2%
(Missing) 832
 
9.8%

Length

2023-10-17T23:48:13.514387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:13.634454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7667
100.0%

Most occurring characters

ValueCountFrequency (%)
0 15334
66.7%
. 7667
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15334
66.7%
Other Punctuation 7667
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15334
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15334
66.7%
. 7667
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15334
66.7%
. 7667
33.3%

HUNTIF
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8499
Missing (%)100.0%
Memory size66.5 KiB

PRION
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing832
Missing (%)9.8%
Memory size66.5 KiB
0.0
7667 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23001
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7667
90.2%
(Missing) 832
 
9.8%

Length

2023-10-17T23:48:13.796498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:13.954879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7667
100.0%

Most occurring characters

ValueCountFrequency (%)
0 15334
66.7%
. 7667
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15334
66.7%
Other Punctuation 7667
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15334
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15334
66.7%
. 7667
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15334
66.7%
. 7667
33.3%

PRIONIF
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8499
Missing (%)100.0%
Memory size66.5 KiB

MEDS
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing835
Missing (%)9.8%
Memory size66.5 KiB
0.0
7578 
1.0
 
86

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22992
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7578
89.2%
1.0 86
 
1.0%
(Missing) 835
 
9.8%

Length

2023-10-17T23:48:14.094543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:14.286863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7578
98.9%
1.0 86
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 15242
66.3%
. 7664
33.3%
1 86
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15328
66.7%
Other Punctuation 7664
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15242
99.4%
1 86
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 7664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22992
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15242
66.3%
. 7664
33.3%
1 86
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15242
66.3%
. 7664
33.3%
1 86
 
0.4%

MEDSIF
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)5.5%
Missing8426
Missing (%)99.1%
Memory size66.5 KiB
2.0
41 
1.0
22 
3.0
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters219
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 41
 
0.5%
1.0 22
 
0.3%
3.0 9
 
0.1%
0.0 1
 
< 0.1%
(Missing) 8426
99.1%

Length

2023-10-17T23:48:14.444536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:14.596796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 41
56.2%
1.0 22
30.1%
3.0 9
 
12.3%
0.0 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 74
33.8%
. 73
33.3%
2 41
18.7%
1 22
 
10.0%
3 9
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 146
66.7%
Other Punctuation 73
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 74
50.7%
2 41
28.1%
1 22
 
15.1%
3 9
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 219
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 74
33.8%
. 73
33.3%
2 41
18.7%
1 22
 
10.0%
3 9
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 74
33.8%
. 73
33.3%
2 41
18.7%
1 22
 
10.0%
3 9
 
4.1%

DYSILL
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing834
Missing (%)9.8%
Memory size66.5 KiB
0.0
7579 
1.0
 
86

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22995
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7579
89.2%
1.0 86
 
1.0%
(Missing) 834
 
9.8%

Length

2023-10-17T23:48:14.764044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:14.906965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7579
98.9%
1.0 86
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 15244
66.3%
. 7665
33.3%
1 86
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15330
66.7%
Other Punctuation 7665
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15244
99.4%
1 86
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 7665
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22995
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15244
66.3%
. 7665
33.3%
1 86
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15244
66.3%
. 7665
33.3%
1 86
 
0.4%

DYSILLIF
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)4.2%
Missing8428
Missing (%)99.2%
Memory size66.5 KiB
2.0
31 
1.0
30 
3.0
10 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters213
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 31
 
0.4%
1.0 30
 
0.4%
3.0 10
 
0.1%
(Missing) 8428
99.2%

Length

2023-10-17T23:48:15.026405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:15.186164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 31
43.7%
1.0 30
42.3%
3.0 10
 
14.1%

Most occurring characters

ValueCountFrequency (%)
. 71
33.3%
0 71
33.3%
2 31
14.6%
1 30
14.1%
3 10
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 142
66.7%
Other Punctuation 71
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 71
50.0%
2 31
21.8%
1 30
21.1%
3 10
 
7.0%
Other Punctuation
ValueCountFrequency (%)
. 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 213
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 71
33.3%
0 71
33.3%
2 31
14.6%
1 30
14.1%
3 10
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 213
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 71
33.3%
0 71
33.3%
2 31
14.6%
1 30
14.1%
3 10
 
4.7%

DEP
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing831
Missing (%)9.8%
Memory size66.5 KiB
0.0
6460 
1.0
1208 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23004
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6460
76.0%
1.0 1208
 
14.2%
(Missing) 831
 
9.8%

Length

2023-10-17T23:48:15.324808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:15.454707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6460
84.2%
1.0 1208
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 14128
61.4%
. 7668
33.3%
1 1208
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15336
66.7%
Other Punctuation 7668
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14128
92.1%
1 1208
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 7668
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23004
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14128
61.4%
. 7668
33.3%
1 1208
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14128
61.4%
. 7668
33.3%
1 1208
 
5.3%

DEPIF
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.5%
Missing7876
Missing (%)92.7%
Memory size66.5 KiB
3.0
374 
2.0
155 
1.0
94 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1869
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row1.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0 374
 
4.4%
2.0 155
 
1.8%
1.0 94
 
1.1%
(Missing) 7876
92.7%

Length

2023-10-17T23:48:15.614794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:15.756680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 374
60.0%
2.0 155
24.9%
1.0 94
 
15.1%

Most occurring characters

ValueCountFrequency (%)
. 623
33.3%
0 623
33.3%
3 374
20.0%
2 155
 
8.3%
1 94
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1246
66.7%
Other Punctuation 623
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 623
50.0%
3 374
30.0%
2 155
 
12.4%
1 94
 
7.5%
Other Punctuation
ValueCountFrequency (%)
. 623
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1869
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 623
33.3%
0 623
33.3%
3 374
20.0%
2 155
 
8.3%
1 94
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 623
33.3%
0 623
33.3%
3 374
20.0%
2 155
 
8.3%
1 94
 
5.0%

OTHPSY
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing831
Missing (%)9.8%
Memory size66.5 KiB
0.0
7621 
1.0
 
47

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23004
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7621
89.7%
1.0 47
 
0.6%
(Missing) 831
 
9.8%

Length

2023-10-17T23:48:15.916939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:16.058541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7621
99.4%
1.0 47
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 15289
66.5%
. 7668
33.3%
1 47
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15336
66.7%
Other Punctuation 7668
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15289
99.7%
1 47
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 7668
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23004
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15289
66.5%
. 7668
33.3%
1 47
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15289
66.5%
. 7668
33.3%
1 47
 
0.2%

OTHPSYIF
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)17.4%
Missing8476
Missing (%)99.7%
Memory size66.5 KiB
2.0
10 
3.0
1.0
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 10
 
0.1%
3.0 8
 
0.1%
1.0 3
 
< 0.1%
0.0 2
 
< 0.1%
(Missing) 8476
99.7%

Length

2023-10-17T23:48:16.203908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:16.366435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 10
43.5%
3.0 8
34.8%
1.0 3
 
13.0%
0.0 2
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 25
36.2%
. 23
33.3%
2 10
 
14.5%
3 8
 
11.6%
1 3
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46
66.7%
Other Punctuation 23
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25
54.3%
2 10
 
21.7%
3 8
 
17.4%
1 3
 
6.5%
Other Punctuation
ValueCountFrequency (%)
. 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25
36.2%
. 23
33.3%
2 10
 
14.5%
3 8
 
11.6%
1 3
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25
36.2%
. 23
33.3%
2 10
 
14.5%
3 8
 
11.6%
1 3
 
4.3%

DOWNS
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing832
Missing (%)9.8%
Memory size66.5 KiB
0.0
7667 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23001
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7667
90.2%
(Missing) 832
 
9.8%

Length

2023-10-17T23:48:16.494601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:16.626805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7667
100.0%

Most occurring characters

ValueCountFrequency (%)
0 15334
66.7%
. 7667
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15334
66.7%
Other Punctuation 7667
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15334
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15334
66.7%
. 7667
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15334
66.7%
. 7667
33.3%

DOWNSIF
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing8498
Missing (%)> 99.9%
Memory size66.5 KiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.0

Common Values

ValueCountFrequency (%)
0.0 1
 
< 0.1%
(Missing) 8498
> 99.9%

Length

2023-10-17T23:48:16.786765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:16.926816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

PARK
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing868
Missing (%)10.2%
Memory size66.5 KiB
0.0
7539 
1.0
 
92

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22893
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7539
88.7%
1.0 92
 
1.1%
(Missing) 868
 
10.2%

Length

2023-10-17T23:48:17.054609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:17.254676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7539
98.8%
1.0 92
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 15170
66.3%
. 7631
33.3%
1 92
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15262
66.7%
Other Punctuation 7631
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15170
99.4%
1 92
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 7631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22893
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15170
66.3%
. 7631
33.3%
1 92
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15170
66.3%
. 7631
33.3%
1 92
 
0.4%

STROKE
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing4511
Missing (%)53.1%
Memory size66.5 KiB
0.0
3922 
1.0
 
66

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11964
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3922
46.1%
1.0 66
 
0.8%
(Missing) 4511
53.1%

Length

2023-10-17T23:48:17.394758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:17.556466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3922
98.3%
1.0 66
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 7910
66.1%
. 3988
33.3%
1 66
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7976
66.7%
Other Punctuation 3988
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7910
99.2%
1 66
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 3988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7910
66.1%
. 3988
33.3%
1 66
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7910
66.1%
. 3988
33.3%
1 66
 
0.6%

STROKIF
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)9.8%
Missing8458
Missing (%)99.5%
Memory size66.5 KiB
3.0
20 
2.0
11 
1.0
0.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 20
 
0.2%
2.0 11
 
0.1%
1.0 8
 
0.1%
0.0 2
 
< 0.1%
(Missing) 8458
99.5%

Length

2023-10-17T23:48:17.680537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:17.796402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 20
48.8%
2.0 11
26.8%
1.0 8
 
19.5%
0.0 2
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 43
35.0%
. 41
33.3%
3 20
16.3%
2 11
 
8.9%
1 8
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82
66.7%
Other Punctuation 41
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43
52.4%
3 20
24.4%
2 11
 
13.4%
1 8
 
9.8%
Other Punctuation
ValueCountFrequency (%)
. 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 123
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43
35.0%
. 41
33.3%
3 20
16.3%
2 11
 
8.9%
1 8
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43
35.0%
. 41
33.3%
3 20
16.3%
2 11
 
8.9%
1 8
 
6.5%

HYCEPH
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing832
Missing (%)9.8%
Memory size66.5 KiB
0.0
7663 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23001
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7663
90.2%
1.0 4
 
< 0.1%
(Missing) 832
 
9.8%

Length

2023-10-17T23:48:17.916178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:18.084863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7663
99.9%
1.0 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15330
66.6%
. 7667
33.3%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15334
66.7%
Other Punctuation 7667
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15330
> 99.9%
1 4
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 7667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15330
66.6%
. 7667
33.3%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15330
66.6%
. 7667
33.3%
1 4
 
< 0.1%

HYCEPHIF
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)75.0%
Missing8495
Missing (%)> 99.9%
Memory size66.5 KiB
0.0
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row0.0
2nd row1.0
3rd row2.0
4th row0.0

Common Values

ValueCountFrequency (%)
0.0 2
 
< 0.1%
1.0 1
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 8495
> 99.9%

Length

2023-10-17T23:48:18.224622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:18.424690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2
50.0%
1.0 1
25.0%
2.0 1
25.0%

Most occurring characters

ValueCountFrequency (%)
0 6
50.0%
. 4
33.3%
1 1
 
8.3%
2 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
66.7%
Other Punctuation 4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6
75.0%
1 1
 
12.5%
2 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6
50.0%
. 4
33.3%
1 1
 
8.3%
2 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6
50.0%
. 4
33.3%
1 1
 
8.3%
2 1
 
8.3%

BRNINJ
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing832
Missing (%)9.8%
Memory size66.5 KiB
0.0
7582 
1.0
 
85

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23001
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7582
89.2%
1.0 85
 
1.0%
(Missing) 832
 
9.8%

Length

2023-10-17T23:48:18.544370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:18.684640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7582
98.9%
1.0 85
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 15249
66.3%
. 7667
33.3%
1 85
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15334
66.7%
Other Punctuation 7667
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15249
99.4%
1 85
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 7667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15249
66.3%
. 7667
33.3%
1 85
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15249
66.3%
. 7667
33.3%
1 85
 
0.4%

BRNINJIF
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)10.8%
Missing8462
Missing (%)99.6%
Memory size66.5 KiB
3.0
20 
1.0
11 
2.0
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters111
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.7%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 20
 
0.2%
1.0 11
 
0.1%
2.0 5
 
0.1%
0.0 1
 
< 0.1%
(Missing) 8462
99.6%

Length

2023-10-17T23:48:18.816498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:19.017129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 20
54.1%
1.0 11
29.7%
2.0 5
 
13.5%
0.0 1
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 38
34.2%
. 37
33.3%
3 20
18.0%
1 11
 
9.9%
2 5
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74
66.7%
Other Punctuation 37
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38
51.4%
3 20
27.0%
1 11
 
14.9%
2 5
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 111
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38
34.2%
. 37
33.3%
3 20
18.0%
1 11
 
9.9%
2 5
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38
34.2%
. 37
33.3%
3 20
18.0%
1 11
 
9.9%
2 5
 
4.5%

NEOP
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing832
Missing (%)9.8%
Memory size66.5 KiB
0.0
7660 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23001
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7660
90.1%
1.0 7
 
0.1%
(Missing) 832
 
9.8%

Length

2023-10-17T23:48:19.164659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:19.349045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7660
99.9%
1.0 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15327
66.6%
. 7667
33.3%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15334
66.7%
Other Punctuation 7667
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15327
> 99.9%
1 7
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 7667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15327
66.6%
. 7667
33.3%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15327
66.6%
. 7667
33.3%
1 7
 
< 0.1%

NEOPIF
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)60.0%
Missing8494
Missing (%)99.9%
Memory size66.5 KiB
3.0
1.0
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)40.0%

Sample

1st row1.0
2nd row3.0
3rd row0.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 3
 
< 0.1%
1.0 1
 
< 0.1%
0.0 1
 
< 0.1%
(Missing) 8494
99.9%

Length

2023-10-17T23:48:19.503795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:19.646223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 3
60.0%
1.0 1
 
20.0%
0.0 1
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 6
40.0%
. 5
33.3%
3 3
20.0%
1 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6
60.0%
3 3
30.0%
1 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6
40.0%
. 5
33.3%
3 3
20.0%
1 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6
40.0%
. 5
33.3%
3 3
20.0%
1 1
 
6.7%

COGOTH
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing832
Missing (%)9.8%
Memory size66.5 KiB
0.0
7455 
1.0
 
212

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23001
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7455
87.7%
1.0 212
 
2.5%
(Missing) 832
 
9.8%

Length

2023-10-17T23:48:19.806550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:19.954495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7455
97.2%
1.0 212
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 15122
65.7%
. 7667
33.3%
1 212
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15334
66.7%
Other Punctuation 7667
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15122
98.6%
1 212
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 7667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15122
65.7%
. 7667
33.3%
1 212
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15122
65.7%
. 7667
33.3%
1 212
 
0.9%

COGOTHIF
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)2.0%
Missing8347
Missing (%)98.2%
Memory size66.5 KiB
1.0
72 
2.0
52 
3.0
28 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters456
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row3.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 72
 
0.8%
2.0 52
 
0.6%
3.0 28
 
0.3%
(Missing) 8347
98.2%

Length

2023-10-17T23:48:20.054758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:20.234481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 72
47.4%
2.0 52
34.2%
3.0 28
 
18.4%

Most occurring characters

ValueCountFrequency (%)
. 152
33.3%
0 152
33.3%
1 72
15.8%
2 52
 
11.4%
3 28
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 304
66.7%
Other Punctuation 152
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 152
50.0%
1 72
23.7%
2 52
 
17.1%
3 28
 
9.2%
Other Punctuation
ValueCountFrequency (%)
. 152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 456
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 152
33.3%
0 152
33.3%
1 72
15.8%
2 52
 
11.4%
3 28
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 152
33.3%
0 152
33.3%
1 72
15.8%
2 52
 
11.4%
3 28
 
6.1%

COGOTH2
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1617
Missing (%)19.0%
Memory size66.5 KiB
0.0
6862 
1.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters20646
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6862
80.7%
1.0 20
 
0.2%
(Missing) 1617
 
19.0%

Length

2023-10-17T23:48:20.366369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:20.574794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6862
99.7%
1.0 20
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 13744
66.6%
. 6882
33.3%
1 20
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13764
66.7%
Other Punctuation 6882
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13744
99.9%
1 20
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 6882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20646
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13744
66.6%
. 6882
33.3%
1 20
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20646
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13744
66.6%
. 6882
33.3%
1 20
 
0.1%

COGOTH2F
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)21.1%
Missing8480
Missing (%)99.8%
Memory size66.5 KiB
2.0
1.0
3.0
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 8
 
0.1%
1.0 7
 
0.1%
3.0 2
 
< 0.1%
0.0 2
 
< 0.1%
(Missing) 8480
99.8%

Length

2023-10-17T23:48:20.724014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:20.916924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 8
42.1%
1.0 7
36.8%
3.0 2
 
10.5%
0.0 2
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 21
36.8%
. 19
33.3%
2 8
 
14.0%
1 7
 
12.3%
3 2
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38
66.7%
Other Punctuation 19
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21
55.3%
2 8
 
21.1%
1 7
 
18.4%
3 2
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21
36.8%
. 19
33.3%
2 8
 
14.0%
1 7
 
12.3%
3 2
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21
36.8%
. 19
33.3%
2 8
 
14.0%
1 7
 
12.3%
3 2
 
3.5%

COGOTH3
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1618
Missing (%)19.0%
Memory size66.5 KiB
0.0
6877 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters20643
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6877
80.9%
1.0 4
 
< 0.1%
(Missing) 1618
 
19.0%

Length

2023-10-17T23:48:21.076761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:21.204563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6877
99.9%
1.0 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 13758
66.6%
. 6881
33.3%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13762
66.7%
Other Punctuation 6881
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13758
> 99.9%
1 4
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 6881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20643
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13758
66.6%
. 6881
33.3%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20643
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13758
66.6%
. 6881
33.3%
1 4
 
< 0.1%

COGOTH3F
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)50.0%
Missing8495
Missing (%)> 99.9%
Memory size66.5 KiB
2.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)25.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0

Common Values

ValueCountFrequency (%)
2.0 3
 
< 0.1%
1.0 1
 
< 0.1%
(Missing) 8495
> 99.9%

Length

2023-10-17T23:48:21.319013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:21.473786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 3
75.0%
1.0 1
 
25.0%

Most occurring characters

ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
2 3
25.0%
1 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
66.7%
Other Punctuation 4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
50.0%
2 3
37.5%
1 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
2 3
25.0%
1 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
2 3
25.0%
1 1
 
8.3%

hiv
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4236
Missing (%)49.8%
Memory size66.5 KiB
0.0
4263 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12789
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4263
50.2%
(Missing) 4236
49.8%

Length

2023-10-17T23:48:21.584866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:21.729365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4263
100.0%

Most occurring characters

ValueCountFrequency (%)
0 8526
66.7%
. 4263
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8526
66.7%
Other Punctuation 4263
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8526
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4263
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12789
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8526
66.7%
. 4263
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12789
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8526
66.7%
. 4263
33.3%

dxmethod
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
1.0
3641 
3.0
 
2
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3641
42.8%
3.0 2
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:21.888184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:22.010047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3641
99.9%
3.0 2
 
0.1%
2.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 3644
33.3%
0 3644
33.3%
1 3641
33.3%
3 2
 
< 0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3644
50.0%
1 3641
50.0%
3 2
 
< 0.1%
2 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3644
33.3%
0 3644
33.3%
1 3641
33.3%
3 2
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3644
33.3%
0 3644
33.3%
1 3641
33.3%
3 2
 
< 0.1%
2 1
 
< 0.1%

amndem
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.4%
Missing7937
Missing (%)93.4%
Memory size66.5 KiB
1.0
550 
0.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1686
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 550
 
6.5%
0.0 12
 
0.1%
(Missing) 7937
93.4%

Length

2023-10-17T23:48:22.135902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:22.266943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 550
97.9%
0.0 12
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 574
34.0%
. 562
33.3%
1 550
32.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1124
66.7%
Other Punctuation 562
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 574
51.1%
1 550
48.9%
Other Punctuation
ValueCountFrequency (%)
. 562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1686
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 574
34.0%
. 562
33.3%
1 550
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 574
34.0%
. 562
33.3%
1 550
32.6%

pca
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.4%
Missing7937
Missing (%)93.4%
Memory size66.5 KiB
0.0
558 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1686
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 558
 
6.6%
1.0 4
 
< 0.1%
(Missing) 7937
93.4%

Length

2023-10-17T23:48:22.373912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:22.546614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 558
99.3%
1.0 4
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1120
66.4%
. 562
33.3%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1124
66.7%
Other Punctuation 562
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1120
99.6%
1 4
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1686
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1120
66.4%
. 562
33.3%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1120
66.4%
. 562
33.3%
1 4
 
0.2%

ppasyn
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.4%
Missing7937
Missing (%)93.4%
Memory size66.5 KiB
0.0
556 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1686
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 556
 
6.5%
1.0 6
 
0.1%
(Missing) 7937
93.4%

Length

2023-10-17T23:48:22.699107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:22.852504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 556
98.9%
1.0 6
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 1118
66.3%
. 562
33.3%
1 6
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1124
66.7%
Other Punctuation 562
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1118
99.5%
1 6
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1686
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1118
66.3%
. 562
33.3%
1 6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1118
66.3%
. 562
33.3%
1 6
 
0.4%

ppasynt
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)50.0%
Missing8493
Missing (%)99.9%
Memory size66.5 KiB
2.0
1.0
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)16.7%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 3
 
< 0.1%
1.0 2
 
< 0.1%
3.0 1
 
< 0.1%
(Missing) 8493
99.9%

Length

2023-10-17T23:48:23.002599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:23.146175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 3
50.0%
1.0 2
33.3%
3.0 1
 
16.7%

Most occurring characters

ValueCountFrequency (%)
. 6
33.3%
0 6
33.3%
2 3
16.7%
1 2
 
11.1%
3 1
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12
66.7%
Other Punctuation 6
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6
50.0%
2 3
25.0%
1 2
 
16.7%
3 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 6
33.3%
0 6
33.3%
2 3
16.7%
1 2
 
11.1%
3 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 6
33.3%
0 6
33.3%
2 3
16.7%
1 2
 
11.1%
3 1
 
5.6%

ftdsyn
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.4%
Missing7937
Missing (%)93.4%
Memory size66.5 KiB
0.0
560 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1686
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 560
 
6.6%
1.0 2
 
< 0.1%
(Missing) 7937
93.4%

Length

2023-10-17T23:48:23.274496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:23.404900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 560
99.6%
1.0 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1122
66.5%
. 562
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1124
66.7%
Other Punctuation 562
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1122
99.8%
1 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1686
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1122
66.5%
. 562
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1122
66.5%
. 562
33.3%
1 2
 
0.1%

lbdsyn
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.4%
Missing7937
Missing (%)93.4%
Memory size66.5 KiB
0.0
556 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1686
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 556
 
6.5%
1.0 6
 
0.1%
(Missing) 7937
93.4%

Length

2023-10-17T23:48:23.535982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:23.707075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 556
98.9%
1.0 6
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 1118
66.3%
. 562
33.3%
1 6
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1124
66.7%
Other Punctuation 562
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1118
99.5%
1 6
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1686
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1118
66.3%
. 562
33.3%
1 6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1118
66.3%
. 562
33.3%
1 6
 
0.4%

namndem
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.4%
Missing7937
Missing (%)93.4%
Memory size66.5 KiB
0.0
561 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1686
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 561
 
6.6%
1.0 1
 
< 0.1%
(Missing) 7937
93.4%

Length

2023-10-17T23:48:23.846701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:24.027042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 561
99.8%
1.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1123
66.6%
. 562
33.3%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1124
66.7%
Other Punctuation 562
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1123
99.9%
1 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1686
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1123
66.6%
. 562
33.3%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1123
66.6%
. 562
33.3%
1 1
 
0.1%

amylpet
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:24.174716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:24.298089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

amylcsf
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:24.426950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:24.637083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

fdgad
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:24.766944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:24.927237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

hippatr
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:25.066807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:25.196453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

taupetad
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:25.334715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:25.477938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

csftau
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:25.616887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:25.748789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

fdgftld
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:25.864537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:26.014107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

tpetftld
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:26.123829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:26.264857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

mrftld
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:26.394854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:26.546991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

datscan
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:26.697537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:26.856571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

othbiom
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
0.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:27.004507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:27.196417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7286
66.7%
. 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7286
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7286
66.7%
. 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7286
66.7%
. 3643
33.3%

imaglinf
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:27.344856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:27.496970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

imaglac
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:27.636310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:27.779114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

imagmach
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:27.886728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:28.054722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

imagmich
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:28.194437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:28.325809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

imagmwmh
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:28.454516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:29.194426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

imagewmh
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
8.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:29.311527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:29.464887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3643
33.3%
. 3643
33.3%
0 3643
33.3%

admut
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
9.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:29.584946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:29.726820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
9.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
9 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 3643
33.3%
. 3643
33.3%
0 3643
33.3%

ftldmut
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
9.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:29.840252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:30.012059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
9.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
9 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 3643
33.3%
. 3643
33.3%
0 3643
33.3%

othmut
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4856
Missing (%)57.1%
Memory size66.5 KiB
9.0
3643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10929
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 3643
42.9%
(Missing) 4856
57.1%

Length

2023-10-17T23:48:30.164567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:30.318727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
9.0 3643
100.0%

Most occurring characters

ValueCountFrequency (%)
9 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7286
66.7%
Other Punctuation 3643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 3643
50.0%
0 3643
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 3643
33.3%
. 3643
33.3%
0 3643
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 3643
33.3%
. 3643
33.3%
0 3643
33.3%

alzdis
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3075 
1.0
569 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3075
36.2%
1.0 569
 
6.7%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:30.474484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:30.669585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3075
84.4%
1.0 569
 
15.6%

Most occurring characters

ValueCountFrequency (%)
0 6719
61.5%
. 3644
33.3%
1 569
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6719
92.2%
1 569
 
7.8%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6719
61.5%
. 3644
33.3%
1 569
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6719
61.5%
. 3644
33.3%
1 569
 
5.2%

alzdisif
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.4%
Missing7930
Missing (%)93.3%
Memory size66.5 KiB
1.0
563 
2.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1707
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 563
 
6.6%
2.0 6
 
0.1%
(Missing) 7930
93.3%

Length

2023-10-17T23:48:30.847115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:30.998926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 563
98.9%
2.0 6
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 569
33.3%
0 569
33.3%
1 563
33.0%
2 6
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1138
66.7%
Other Punctuation 569
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 569
50.0%
1 563
49.5%
2 6
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 569
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1707
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 569
33.3%
0 569
33.3%
1 563
33.0%
2 6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1707
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 569
33.3%
0 569
33.3%
1 563
33.0%
2 6
 
0.4%

lbdis
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3629 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3629
42.7%
1.0 15
 
0.2%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:31.131159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:31.244645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3629
99.6%
1.0 15
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 7273
66.5%
. 3644
33.3%
1 15
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7273
99.8%
1 15
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7273
66.5%
. 3644
33.3%
1 15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7273
66.5%
. 3644
33.3%
1 15
 
0.1%

lbdif
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)13.3%
Missing8484
Missing (%)99.8%
Memory size66.5 KiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 8
 
0.1%
2.0 7
 
0.1%
(Missing) 8484
99.8%

Length

2023-10-17T23:48:31.384748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:31.517036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8
53.3%
2.0 7
46.7%

Most occurring characters

ValueCountFrequency (%)
. 15
33.3%
0 15
33.3%
1 8
17.8%
2 7
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30
66.7%
Other Punctuation 15
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15
50.0%
1 8
26.7%
2 7
23.3%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 15
33.3%
0 15
33.3%
1 8
17.8%
2 7
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15
33.3%
0 15
33.3%
1 8
17.8%
2 7
15.6%

msa
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3644 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3644
42.9%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:31.634661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:31.794600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3644
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7288
66.7%
. 3644
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7288
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7288
66.7%
. 3644
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7288
66.7%
. 3644
33.3%

msaif
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8499
Missing (%)100.0%
Memory size66.5 KiB

ftldmo
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3644 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3644
42.9%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:31.954504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:32.076798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3644
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7288
66.7%
. 3644
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7288
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7288
66.7%
. 3644
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7288
66.7%
. 3644
33.3%

ftldmoif
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8499
Missing (%)100.0%
Memory size66.5 KiB

ftldnos
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3639 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3639
42.8%
1.0 5
 
0.1%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:32.177108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:32.314889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3639
99.9%
1.0 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7283
66.6%
. 3644
33.3%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7283
99.9%
1 5
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7283
66.6%
. 3644
33.3%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7283
66.6%
. 3644
33.3%
1 5
 
< 0.1%

ftldnoif
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)40.0%
Missing8494
Missing (%)99.9%
Memory size66.5 KiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)20.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 4
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 8494
99.9%

Length

2023-10-17T23:48:32.507408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:32.686522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4
80.0%
2.0 1
 
20.0%

Most occurring characters

ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
1 4
26.7%
2 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5
50.0%
1 4
40.0%
2 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
1 4
26.7%
2 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
1 4
26.7%
2 1
 
6.7%

ftldsubt
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)42.9%
Missing8492
Missing (%)99.9%
Memory size66.5 KiB
9.0
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)14.3%

Sample

1st row9.0
2nd row9.0
3rd row1.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 4
 
< 0.1%
1.0 2
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 8492
99.9%

Length

2023-10-17T23:48:32.864783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:33.044624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
9.0 4
57.1%
1.0 2
28.6%
2.0 1
 
14.3%

Most occurring characters

ValueCountFrequency (%)
. 7
33.3%
0 7
33.3%
9 4
19.0%
1 2
 
9.5%
2 1
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14
66.7%
Other Punctuation 7
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7
50.0%
9 4
28.6%
1 2
 
14.3%
2 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 7
33.3%
0 7
33.3%
9 4
19.0%
1 2
 
9.5%
2 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 7
33.3%
0 7
33.3%
9 4
19.0%
1 2
 
9.5%
2 1
 
4.8%

cvd
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3581 
1.0
 
63

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3581
42.1%
1.0 63
 
0.7%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:33.167983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:33.316343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3581
98.3%
1.0 63
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 7225
66.1%
. 3644
33.3%
1 63
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7225
99.1%
1 63
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7225
66.1%
. 3644
33.3%
1 63
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7225
66.1%
. 3644
33.3%
1 63
 
0.6%

cvdif
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)8.3%
Missing8463
Missing (%)99.6%
Memory size66.5 KiB
1.0
18 
3.0
10 
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters108
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 18
 
0.2%
3.0 10
 
0.1%
2.0 8
 
0.1%
(Missing) 8463
99.6%

Length

2023-10-17T23:48:33.446687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:33.664770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 18
50.0%
3.0 10
27.8%
2.0 8
22.2%

Most occurring characters

ValueCountFrequency (%)
. 36
33.3%
0 36
33.3%
1 18
16.7%
3 10
 
9.3%
2 8
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72
66.7%
Other Punctuation 36
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36
50.0%
1 18
25.0%
3 10
 
13.9%
2 8
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 36
33.3%
0 36
33.3%
1 18
16.7%
3 10
 
9.3%
2 8
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 36
33.3%
0 36
33.3%
1 18
16.7%
3 10
 
9.3%
2 8
 
7.4%

prevstk
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)3.2%
Missing8436
Missing (%)99.3%
Memory size66.5 KiB
1.0
54 
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters189
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 54
 
0.6%
0.0 9
 
0.1%
(Missing) 8436
99.3%

Length

2023-10-17T23:48:33.804485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:33.984433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 54
85.7%
0.0 9
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 72
38.1%
. 63
33.3%
1 54
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126
66.7%
Other Punctuation 63
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72
57.1%
1 54
42.9%
Other Punctuation
ValueCountFrequency (%)
. 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 189
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72
38.1%
. 63
33.3%
1 54
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72
38.1%
. 63
33.3%
1 54
28.6%

strokdec
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)3.4%
Missing8441
Missing (%)99.3%
Memory size66.5 KiB
0.0
37 
1.0
21 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37
 
0.4%
1.0 21
 
0.2%
(Missing) 8441
99.3%

Length

2023-10-17T23:48:34.144466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:34.318702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37
63.8%
1.0 21
36.2%

Most occurring characters

ValueCountFrequency (%)
0 95
54.6%
. 58
33.3%
1 21
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116
66.7%
Other Punctuation 58
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95
81.9%
1 21
 
18.1%
Other Punctuation
ValueCountFrequency (%)
. 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95
54.6%
. 58
33.3%
1 21
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95
54.6%
. 58
33.3%
1 21
 
12.1%

stkimag
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)5.2%
Missing8441
Missing (%)99.3%
Memory size66.5 KiB
9.0
47 
1.0
10 
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.7%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 47
 
0.6%
1.0 10
 
0.1%
0.0 1
 
< 0.1%
(Missing) 8441
99.3%

Length

2023-10-17T23:48:34.483652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:34.674774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
9.0 47
81.0%
1.0 10
 
17.2%
0.0 1
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 59
33.9%
. 58
33.3%
9 47
27.0%
1 10
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116
66.7%
Other Punctuation 58
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59
50.9%
9 47
40.5%
1 10
 
8.6%
Other Punctuation
ValueCountFrequency (%)
. 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59
33.9%
. 58
33.3%
9 47
27.0%
1 10
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59
33.9%
. 58
33.3%
9 47
27.0%
1 10
 
5.7%

infnetw
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)3.2%
Missing8436
Missing (%)99.3%
Memory size66.5 KiB
9.0
61 
0.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters189
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 61
 
0.7%
0.0 2
 
< 0.1%
(Missing) 8436
99.3%

Length

2023-10-17T23:48:34.814519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:34.936377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
9.0 61
96.8%
0.0 2
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 65
34.4%
. 63
33.3%
9 61
32.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126
66.7%
Other Punctuation 63
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65
51.6%
9 61
48.4%
Other Punctuation
ValueCountFrequency (%)
. 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 189
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65
34.4%
. 63
33.3%
9 61
32.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65
34.4%
. 63
33.3%
9 61
32.3%

infwmh
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)3.2%
Missing8436
Missing (%)99.3%
Memory size66.5 KiB
9.0
61 
0.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters189
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 61
 
0.7%
0.0 2
 
< 0.1%
(Missing) 8436
99.3%

Length

2023-10-17T23:48:35.074617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:35.246911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
9.0 61
96.8%
0.0 2
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 65
34.4%
. 63
33.3%
9 61
32.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126
66.7%
Other Punctuation 63
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65
51.6%
9 61
48.4%
Other Punctuation
ValueCountFrequency (%)
. 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 189
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65
34.4%
. 63
33.3%
9 61
32.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65
34.4%
. 63
33.3%
9 61
32.3%

esstrem
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3409 
1.0
 
235

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3409
40.1%
1.0 235
 
2.8%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:35.426479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:35.579302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3409
93.6%
1.0 235
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 7053
64.5%
. 3644
33.3%
1 235
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7053
96.8%
1 235
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7053
64.5%
. 3644
33.3%
1 235
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7053
64.5%
. 3644
33.3%
1 235
 
2.1%

esstreif
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)4.8%
Missing8437
Missing (%)99.3%
Memory size66.5 KiB
3.0
60 
2.0
 
1
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters186
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)3.2%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 60
 
0.7%
2.0 1
 
< 0.1%
1.0 1
 
< 0.1%
(Missing) 8437
99.3%

Length

2023-10-17T23:48:35.736678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:35.896546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 60
96.8%
2.0 1
 
1.6%
1.0 1
 
1.6%

Most occurring characters

ValueCountFrequency (%)
. 62
33.3%
0 62
33.3%
3 60
32.3%
2 1
 
0.5%
1 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124
66.7%
Other Punctuation 62
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 62
50.0%
3 60
48.4%
2 1
 
0.8%
1 1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 62
33.3%
0 62
33.3%
3 60
32.3%
2 1
 
0.5%
1 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 62
33.3%
0 62
33.3%
3 60
32.3%
2 1
 
0.5%
1 1
 
0.5%

brnincte
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)10.0%
Missing8479
Missing (%)99.8%
Memory size66.5 KiB
0.0
15 
9.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15
 
0.2%
9.0 5
 
0.1%
(Missing) 8479
99.8%

Length

2023-10-17T23:48:36.014760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:36.147010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15
75.0%
9.0 5
 
25.0%

Most occurring characters

ValueCountFrequency (%)
0 35
58.3%
. 20
33.3%
9 5
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40
66.7%
Other Punctuation 20
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35
87.5%
9 5
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35
58.3%
. 20
33.3%
9 5
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35
58.3%
. 20
33.3%
9 5
 
8.3%

epilep
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3624 
1.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3624
42.6%
1.0 20
 
0.2%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:36.277416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:36.446597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3624
99.5%
1.0 20
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 7268
66.5%
. 3644
33.3%
1 20
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7268
99.7%
1 20
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7268
66.5%
. 3644
33.3%
1 20
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7268
66.5%
. 3644
33.3%
1 20
 
0.2%

epilepif
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)30.0%
Missing8489
Missing (%)99.9%
Memory size66.5 KiB
3.0
2.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0 6
 
0.1%
2.0 3
 
< 0.1%
1.0 1
 
< 0.1%
(Missing) 8489
99.9%

Length

2023-10-17T23:48:36.590583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:36.717231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 6
60.0%
2.0 3
30.0%
1.0 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
. 10
33.3%
0 10
33.3%
3 6
20.0%
2 3
 
10.0%
1 1
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20
66.7%
Other Punctuation 10
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10
50.0%
3 6
30.0%
2 3
 
15.0%
1 1
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 10
33.3%
0 10
33.3%
3 6
20.0%
2 3
 
10.0%
1 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 10
33.3%
0 10
33.3%
3 6
20.0%
2 3
 
10.0%
1 1
 
3.3%

neopstat
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing8496
Missing (%)> 99.9%
Memory size66.5 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0

Common Values

ValueCountFrequency (%)
1.0 3
 
< 0.1%
(Missing) 8496
> 99.9%

Length

2023-10-17T23:48:36.830010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:36.959510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6
66.7%
Other Punctuation 3
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
50.0%
0 3
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

hivif
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8499
Missing (%)100.0%
Memory size66.5 KiB

othcog
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3627 
1.0
 
17

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3627
42.7%
1.0 17
 
0.2%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:37.077173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:37.268855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3627
99.5%
1.0 17
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 7271
66.5%
. 3644
33.3%
1 17
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7271
99.8%
1 17
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7271
66.5%
. 3644
33.3%
1 17
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7271
66.5%
. 3644
33.3%
1 17
 
0.2%

othcogif
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)11.8%
Missing8482
Missing (%)99.8%
Memory size66.5 KiB
1.0
13 
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters51
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 13
 
0.2%
2.0 4
 
< 0.1%
(Missing) 8482
99.8%

Length

2023-10-17T23:48:37.437185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:37.614864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 13
76.5%
2.0 4
 
23.5%

Most occurring characters

ValueCountFrequency (%)
. 17
33.3%
0 17
33.3%
1 13
25.5%
2 4
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34
66.7%
Other Punctuation 17
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17
50.0%
1 13
38.2%
2 4
 
11.8%
Other Punctuation
ValueCountFrequency (%)
. 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 17
33.3%
0 17
33.3%
1 13
25.5%
2 4
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 17
33.3%
0 17
33.3%
1 13
25.5%
2 4
 
7.8%

deptreat
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.4%
Missing8044
Missing (%)94.6%
Memory size66.5 KiB
1.0
365 
0.0
90 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1365
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 365
 
4.3%
0.0 90
 
1.1%
(Missing) 8044
94.6%

Length

2023-10-17T23:48:37.744649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:37.923836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 365
80.2%
0.0 90
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0 545
39.9%
. 455
33.3%
1 365
26.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 910
66.7%
Other Punctuation 455
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 545
59.9%
1 365
40.1%
Other Punctuation
ValueCountFrequency (%)
. 455
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1365
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 545
39.9%
. 455
33.3%
1 365
26.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1365
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 545
39.9%
. 455
33.3%
1 365
26.7%

bipoldx
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3622 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3622
42.6%
1.0 22
 
0.3%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:38.039192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:38.229079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3622
99.4%
1.0 22
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 7266
66.5%
. 3644
33.3%
1 22
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7266
99.7%
1 22
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7266
66.5%
. 3644
33.3%
1 22
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7266
66.5%
. 3644
33.3%
1 22
 
0.2%

bipoldif
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)40.0%
Missing8494
Missing (%)99.9%
Memory size66.5 KiB
2.0
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 3
 
< 0.1%
3.0 2
 
< 0.1%
(Missing) 8494
99.9%

Length

2023-10-17T23:48:38.344620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:38.496381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 3
60.0%
3.0 2
40.0%

Most occurring characters

ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
3 2
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5
50.0%
2 3
30.0%
3 2
 
20.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
3 2
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
3 2
 
13.3%

schizop
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3640 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3640
42.8%
1.0 4
 
< 0.1%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:38.618858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:38.796241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3640
99.9%
1.0 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7284
66.6%
. 3644
33.3%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7284
99.9%
1 4
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7284
66.6%
. 3644
33.3%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7284
66.6%
. 3644
33.3%
1 4
 
< 0.1%

schizoif
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct2
Distinct (%)100.0%
Missing8497
Missing (%)> 99.9%
Memory size66.5 KiB
3.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row3.0
2nd row2.0

Common Values

ValueCountFrequency (%)
3.0 1
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 8497
> 99.9%

Length

2023-10-17T23:48:38.943070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:39.164535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 1
50.0%
2.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
3 1
16.7%
2 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
50.0%
3 1
25.0%
2 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
3 1
16.7%
2 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
3 1
16.7%
2 1
16.7%

anxiet
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3542 
1.0
 
102

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3542
41.7%
1.0 102
 
1.2%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:39.304015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:39.484753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3542
97.2%
1.0 102
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 7186
65.7%
. 3644
33.3%
1 102
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7186
98.6%
1 102
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7186
65.7%
. 3644
33.3%
1 102
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7186
65.7%
. 3644
33.3%
1 102
 
0.9%

anxietif
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)7.0%
Missing8456
Missing (%)99.5%
Memory size66.5 KiB
2.0
18 
3.0
17 
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters129
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 18
 
0.2%
3.0 17
 
0.2%
1.0 8
 
0.1%
(Missing) 8456
99.5%

Length

2023-10-17T23:48:39.634586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:39.796871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 18
41.9%
3.0 17
39.5%
1.0 8
18.6%

Most occurring characters

ValueCountFrequency (%)
. 43
33.3%
0 43
33.3%
2 18
14.0%
3 17
 
13.2%
1 8
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 86
66.7%
Other Punctuation 43
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43
50.0%
2 18
20.9%
3 17
 
19.8%
1 8
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 129
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 43
33.3%
0 43
33.3%
2 18
14.0%
3 17
 
13.2%
1 8
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 43
33.3%
0 43
33.3%
2 18
14.0%
3 17
 
13.2%
1 8
 
6.2%

delir
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3644 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3644
42.9%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:39.956449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:40.104564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3644
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7288
66.7%
. 3644
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7288
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7288
66.7%
. 3644
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7288
66.7%
. 3644
33.3%

delirif
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8499
Missing (%)100.0%
Memory size66.5 KiB

ptsddx
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3642 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3642
42.9%
1.0 2
 
< 0.1%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:40.210354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:40.366902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3642
99.9%
1.0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7286
66.6%
. 3644
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7286
> 99.9%
1 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7286
66.6%
. 3644
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7286
66.6%
. 3644
33.3%
1 2
 
< 0.1%

ptsddxif
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing8498
Missing (%)> 99.9%
Memory size66.5 KiB
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row3.0

Common Values

ValueCountFrequency (%)
3.0 1
 
< 0.1%
(Missing) 8498
> 99.9%

Length

2023-10-17T23:48:40.496872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:40.613762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
3 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1
33.3%
. 1
33.3%
0 1
33.3%

alcabuse
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)12.5%
Missing8475
Missing (%)99.7%
Memory size66.5 KiB
1.0
21 
0.0
 
2
9.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters72
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row9.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 21
 
0.2%
0.0 2
 
< 0.1%
9.0 1
 
< 0.1%
(Missing) 8475
99.7%

Length

2023-10-17T23:48:40.762673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:40.916379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 21
87.5%
0.0 2
 
8.3%
9.0 1
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 26
36.1%
. 24
33.3%
1 21
29.2%
9 1
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48
66.7%
Other Punctuation 24
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26
54.2%
1 21
43.8%
9 1
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26
36.1%
. 24
33.3%
1 21
29.2%
9 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26
36.1%
. 24
33.3%
1 21
29.2%
9 1
 
1.4%

impsub
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing4855
Missing (%)57.1%
Memory size66.5 KiB
0.0
3639 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10932
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3639
42.8%
1.0 5
 
0.1%
(Missing) 4855
57.1%

Length

2023-10-17T23:48:41.016080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:41.126632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3639
99.9%
1.0 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7283
66.6%
. 3644
33.3%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7288
66.7%
Other Punctuation 3644
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7283
99.9%
1 5
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 3644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7283
66.6%
. 3644
33.3%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7283
66.6%
. 3644
33.3%
1 5
 
< 0.1%

impsubif
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)60.0%
Missing8494
Missing (%)99.9%
Memory size66.5 KiB
2.0
1.0
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)20.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 2
 
< 0.1%
1.0 2
 
< 0.1%
3.0 1
 
< 0.1%
(Missing) 8494
99.9%

Length

2023-10-17T23:48:41.210229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T23:48:41.295219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 2
40.0%
1.0 2
40.0%
3.0 1
20.0%

Most occurring characters

ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 2
 
13.3%
1 2
 
13.3%
3 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5
50.0%
2 2
 
20.0%
1 2
 
20.0%
3 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 2
 
13.3%
1 2
 
13.3%
3 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 2
 
13.3%
1 2
 
13.3%
3 1
 
6.7%

Interactions

2023-10-17T23:47:18.897022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T23:47:18.483719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T23:47:19.244741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T23:47:18.719422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-17T23:48:41.504507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
days_to_visitage at visitWHODIDDXNORMCOGDEMENTEDMCIAMEMMCIAPLUSMCIAPLANMCIAPATTMCIAPEXMCIAPVISMCINON1MCIN1LANMCIN1ATTMCIN1EXMCINON2MCIN2LANMCIN2ATTMCIN2EXIMPNOMCIPROBADPROBADIFPOSSADPOSSADIFDLBDLBIFVASCVASCIFVASCPSVASCPSIFALCDEMALCDEMIFDEMUNDEMUNIFFTDFTDIFPPAPHPPAPHIFPNAPHPPAOTHRPSPCORTMEDSMEDSIFDYSILLDYSILLIFDEPDEPIFOTHPSYOTHPSYIFPARKSTROKESTROKIFHYCEPHHYCEPHIFBRNINJBRNINJIFNEOPNEOPIFCOGOTHCOGOTHIFCOGOTH2COGOTH2FCOGOTH3COGOTH3Fdxmethodamndempcappasynppasyntftdsynlbdsynnamndemalzdisalzdisiflbdislbdifftldnosftldnoifftldsubtcvdcvdifprevstkstrokdecstkimaginfnetwinfwmhesstremesstreifbrnincteepilepepilepifothcogothcogifdeptreatbipoldxbipoldifschizopschizoifanxietanxietifptsddxalcabuseimpsubimpsubif
days_to_visit1.0000.3670.0000.0710.0820.1300.0690.2250.1020.2220.2990.0000.0000.0000.4010.0000.0000.2830.0000.0000.1770.1380.0720.0000.0000.0000.0000.3720.1890.0000.0000.0000.1210.0000.0000.0000.2721.0000.5000.5000.0000.0000.0310.0000.0510.0000.0270.1110.0000.3300.0640.0350.2510.0001.0000.0000.0000.0000.0000.0110.2570.0000.0000.0001.0000.0000.0470.0000.1420.0000.0000.0000.0000.1190.1550.0740.3480.0000.0000.0000.0190.0000.0000.1630.0000.0000.0000.0620.0000.1910.0940.2830.0000.2630.0000.0000.0000.0001.0000.0250.0000.0000.0000.0000.500
age at visit0.3671.0000.0000.2490.0610.0520.0000.1140.0630.0000.0000.0000.8560.0000.0000.0000.4330.3130.0000.0000.0000.0000.1180.0000.0000.0000.0000.3270.0000.0000.0000.0000.0450.0000.0570.9050.0720.3330.8660.1770.0000.0000.0210.2670.0380.0000.0550.0000.0340.1940.0950.0550.3830.0081.0000.0170.0000.0000.0000.0080.2020.0000.0000.0681.0000.0000.0000.0000.0000.0000.0000.0000.0000.2390.0000.0460.0000.0000.0000.0000.0900.1390.0000.3880.0000.0000.0000.0700.0000.0000.0430.0000.0000.0000.1370.0530.0000.0861.0000.0420.2330.0000.0000.0120.000
WHODIDDX0.0000.0001.0000.0000.0220.0001.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0001.0001.0000.0000.0071.0000.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0890.0000.0001.0001.0001.0001.0000.0000.0001.0000.0001.0000.0000.0550.0001.0000.0000.0001.0000.0001.0000.0290.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
NORMCOG0.0710.2490.0001.0000.2310.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0001.0001.0000.0000.1160.0000.0790.0000.0001.0000.0001.0000.0001.0000.0770.4900.0001.0000.0000.0000.0001.0001.0001.0000.0150.0000.1430.6810.1400.0000.2200.1860.0290.4180.1210.0740.2160.0000.0000.0390.0000.0001.0000.1800.2260.0741.0000.0341.0000.0291.0001.0001.0001.0001.0001.0001.0000.8500.0000.1211.0000.0621.0001.0000.1130.1330.0000.6110.0000.0000.0000.0280.0000.0000.0380.0000.1301.0000.0610.0001.0000.0001.0000.0891.0000.0000.6220.0621.000
DEMENTED0.0820.0610.0220.2311.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0001.0001.0000.0960.6280.1230.2050.0820.0650.0000.0000.1540.0000.0000.0770.6470.0000.0000.0001.0000.0001.0000.0000.0000.0000.0000.1430.5040.2080.5370.0760.4710.0850.3010.0520.0000.4280.0001.0000.0500.0000.0160.0000.2570.5210.1120.5550.0761.0000.0451.0001.0001.0001.0001.0001.0001.0000.6590.0000.0300.0000.0340.0000.5240.0000.0460.0000.0000.0001.0001.0000.0000.1640.0000.0560.0000.1950.1490.0000.0340.0000.0001.0000.1600.6511.0000.0000.0000.000
MCIAMEM0.1300.0520.0000.0000.0001.0000.2460.0000.0000.1620.0000.0391.0000.0000.0000.0250.0000.0000.2100.4780.0000.0000.0940.0000.0001.0000.0001.0000.0001.0000.0000.0000.0001.0000.0001.0001.0000.0000.000NaN1.0001.0000.0000.0000.0000.0000.1520.0490.0000.1510.0000.0000.2341.0000.0000.0000.0000.0001.0000.0000.1620.0441.0000.0001.0000.0001.0001.0001.0000.0001.0001.0001.0000.2060.0000.000NaN0.0001.0001.0000.0000.5000.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0510.0590.0000.0001.0000.000NaN0.0920.4331.0000.0000.0001.000
MCIAPLUS0.0690.0001.0000.0000.0000.2461.0000.0000.0000.1940.0000.0641.0000.0000.0000.0470.0000.0000.0000.5960.3480.2450.2850.2250.0001.0000.0001.0000.1041.0000.0701.0000.0541.0000.0001.0001.0000.0000.000NaN1.0001.0000.0370.0000.0000.2420.0000.0470.0550.6350.0000.0000.0001.0000.0000.0000.3080.0000.0000.0470.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0001.0001.0000.1390.0000.000NaN0.0001.0001.0000.0000.0000.0000.1891.0001.0001.0000.0000.0000.0000.0211.0000.0590.0000.2220.0001.0000.000NaN0.0000.0001.0001.0000.0001.000
MCIAPLAN0.2250.1141.0000.0000.0000.0000.0001.0000.0000.2250.1371.0001.0001.0001.0001.0001.0001.0001.0000.0000.0001.0000.0000.0000.000NaN0.000NaN0.2871.0001.0000.0000.0001.0001.0000.0001.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.2890.0000.0000.0000.0000.0000.0001.0000.0000.000NaN1.0000.0000.0000.0000.0000.0000.000NaN1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaN0.000NaNNaN0.0001.0001.0001.0001.0001.0001.0000.0001.000NaN1.0000.0000.0000.0000.0001.0000.0001.0000.0000.0000.6161.0000.0000.000NaN
MCIAPATT0.1020.0631.0000.0000.0000.0000.0000.0001.0000.0000.0631.0001.0001.0001.0001.0001.0001.0001.0000.0000.2361.0000.0000.0000.000NaN0.000NaN0.0001.0001.0000.0000.0001.0001.0000.0001.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0001.0000.0000.000NaN1.0000.0000.0000.0000.0000.0000.000NaN1.0000.0000.0000.0000.0000.0000.0000.0000.2750.0990.000NaN0.000NaNNaN0.0000.0000.0001.0001.0001.0001.0000.0001.000NaN1.0000.0000.2130.0000.0001.0000.0001.0000.0000.0320.5241.0000.0000.000NaN
MCIAPEX0.2220.0001.0000.0000.0000.1620.1940.2250.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.0000.0791.0000.1490.0000.000NaN0.000NaN0.2191.0001.0000.0000.0001.0001.0000.0001.0000.0000.0000.0001.0001.0000.0001.0000.0000.0000.1610.0000.0000.0000.0000.0000.0001.0000.0000.000NaN1.0000.0000.0000.0000.0000.0000.000NaN1.000NaNNaNNaN0.000NaNNaNNaN0.0000.0000.000NaN0.000NaNNaN0.0000.0000.0001.0001.0001.0001.0000.0000.000NaN1.0000.0000.0000.0000.0001.0000.0001.0000.0000.0000.3161.0000.0000.000NaN
MCIAPVIS0.2990.0001.0000.0000.0000.0000.0000.1370.0630.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.1230.2891.0000.0001.0000.000NaN0.000NaN0.0001.0001.0000.0000.0001.0001.0000.0001.0000.0000.0000.0001.0001.0000.0000.0000.0001.0000.0560.2290.2340.0000.0000.0000.0001.0000.0000.083NaN1.0000.0000.0000.0000.0001.0000.000NaN1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaN0.000NaNNaN0.0000.0000.0001.0001.0001.0001.0000.3210.000NaN1.0000.0000.0001.0000.0001.0000.0001.0000.0000.0001.0001.0000.0000.000NaN
MCINON10.0000.0000.0000.0000.0000.0390.0641.0001.0001.0001.0001.0000.0000.0000.5710.0001.0001.0001.0000.1660.0001.0000.0000.0000.0001.0000.0001.0000.0001.0000.0600.0000.1251.0000.0001.0001.0000.0000.000NaN1.0001.0000.0000.0000.0001.0000.0000.0000.0001.0000.0970.0001.0001.0000.0000.0001.0000.0680.0000.0620.1210.0000.0000.0001.0000.0001.0001.0001.0000.0001.0001.0001.0000.0561.0000.000NaN0.0421.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0000.0000.0001.0000.0000.0420.0000.000NaN0.0000.0001.0000.0000.0001.000
MCIN1LAN0.0000.8561.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0000.0001.0000.0001.000NaN1.0001.0001.0000.0001.0000.0000.0000.0001.0001.0000.0001.0001.0000.0000.0001.0001.0000.0000.0001.0000.0001.0000.0001.0000.0000.000NaN0.0001.0001.000NaN1.0000.0001.0000.0000.0000.0000.0000.0000.0000.0001.000NaN1.0000.0001.000NaNNaN1.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.000NaN1.0000.0001.0001.000NaN1.0000.0001.0001.0001.000NaN1.0000.000
MCIN1ATT0.0000.0001.0001.0001.0000.0000.0001.0001.0001.0001.0000.0001.0001.0000.3881.0001.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0000.0001.0000.0000.000NaN1.0001.0001.0000.0001.0000.0000.0000.0001.0001.0000.0001.0001.0000.0000.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0000.000NaN0.0000.0000.000NaN1.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000NaN1.0000.0000.000NaNNaN1.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000NaN1.0000.0001.0000.000NaN1.0000.0000.0001.0001.000NaN1.0000.000
MCIN1EX0.4010.0001.0001.0001.0000.0000.0001.0001.0001.0001.0000.5711.0000.3881.0001.0001.0001.0001.0000.0001.0000.0000.0001.0001.0000.0001.0000.0001.0000.0000.000NaN0.0001.0001.0000.0001.0000.0000.0000.0001.0001.0000.0000.0001.0000.0000.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0000.000NaN0.0000.0830.000NaN1.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000NaN1.0000.0000.000NaNNaN1.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000NaN1.0000.0001.0000.000NaN1.0000.0000.0001.0001.000NaN1.0000.000
MCINON20.0000.0000.0000.0000.0000.0250.0471.0001.0000.0001.0000.0001.0001.0001.0001.0000.0000.3020.8880.1490.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0860.0000.0001.0000.0001.0001.0000.0000.000NaN1.0001.0000.0001.0000.0001.0000.0560.1830.0001.0000.0000.0470.0001.0000.0000.0001.0000.0001.0000.0000.0000.0000.0000.0001.0000.0001.0001.0001.0000.0001.0001.0001.0000.0731.0000.000NaN0.0001.0001.0000.2300.3310.0000.4551.0001.0001.0000.0460.0001.0000.0001.0000.0001.0000.0000.0180.0000.000NaN0.0000.0001.0000.0000.0001.000
MCIN2LAN0.0000.4331.0001.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.3020.0001.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.0001.0001.0000.0001.0000.0001.0000.0000.0000.0001.0001.0001.0000.0001.0000.0000.0000.0001.0000.0001.0001.000NaN1.0000.0001.0000.0001.0000.0000.0001.0000.000NaN1.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000NaN1.0000.0001.0000.0000.0000.0000.0001.0001.0001.0001.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.000NaN1.0000.0000.0001.0001.0001.0001.0000.000
MCIN2ATT0.2830.3131.0001.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.3020.3021.0000.3021.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.0001.0001.0000.0001.0000.0001.0000.0000.0000.0001.0001.0001.0000.0001.0000.0000.0000.0001.0000.0001.0001.000NaN1.0000.0001.0000.0001.0000.0000.0001.0000.000NaN1.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000NaN1.0000.0001.0000.0000.0000.0000.0001.0001.0001.0001.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.000NaN1.0000.0000.0001.0001.0001.0001.0000.000
MCIN2EX0.0000.0001.0001.0001.0000.2100.0001.0001.0001.0001.0001.0001.0001.0001.0000.8880.0000.3021.0000.3521.0000.0000.0001.0001.0000.0001.0000.0001.0000.0000.0001.0001.0000.0001.0000.0001.0000.0000.0000.0001.0001.0001.0000.0001.0000.0000.0000.0001.0000.0001.0000.000NaN1.0000.0001.0000.0001.0000.0000.0001.0000.000NaN1.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000NaN1.0000.0001.0000.0000.0000.0001.0001.0001.0001.0001.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.000NaN1.0000.0000.0001.0001.0001.0001.0000.000
IMPNOMCI0.0000.0000.0000.0000.0960.4780.5960.0000.0000.0000.1230.1661.0001.0000.0000.1491.0001.0000.3521.0000.3070.2720.2670.1630.0721.0000.0001.0000.1391.0000.0000.0000.2061.0000.0001.0001.0000.000NaN1.0001.0001.0000.0000.0000.0430.3630.1040.0000.1880.2680.0580.0480.0001.0000.0000.0830.0000.0240.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0001.0001.0000.2800.0000.000NaN0.0001.0001.0000.0000.0000.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.1010.0000.0000.000NaN0.0380.0001.0000.0000.0001.000
PROBAD0.1770.0000.0070.1160.6280.0000.3480.0000.2360.0790.2890.0001.0001.0001.0000.0001.0001.0001.0000.3071.0000.7040.1281.0000.0430.0470.0570.7080.0000.0000.0440.0000.2401.0000.1221.0000.0000.0000.0000.0001.0000.0000.1440.4870.2210.1070.1430.4430.0090.4160.0520.0000.5311.000NaN0.1040.3460.0000.0000.2030.5580.0290.0000.0061.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
PROBADIF0.1380.0001.0000.0000.1230.0000.2451.0001.0001.0001.0001.0000.0000.0000.0001.0000.0000.0000.0000.2720.7041.0000.2071.0000.4700.8090.0001.0000.0001.0000.000NaN1.0000.0001.0000.0000.1500.0000.0000.0001.0001.0000.0001.0000.0000.0000.0870.3400.0001.0000.0430.0001.0001.000NaN0.0001.0000.0001.0000.1340.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
POSSAD0.0720.1180.0000.0790.2050.0940.2850.0000.0000.1490.0000.0001.0001.0000.0000.0001.0001.0000.0000.2670.1280.2071.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1470.0000.1001.0000.000NaN1.0001.0001.0000.0000.0890.0000.0630.2150.0510.1420.0000.0000.0000.0000.2781.0000.0000.0000.0000.0001.0000.0540.3190.0000.4240.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
POSSADIF0.0000.0000.0000.0000.0820.0000.2250.0000.0000.0001.0000.0001.0001.0001.0001.0000.0000.0001.0000.1631.0001.0000.0001.0000.1000.7000.1900.0000.0001.0000.0001.0000.2020.5540.000NaN1.0000.0000.0000.0001.0000.0000.1740.0000.0660.5780.1390.6980.0000.0000.0740.0630.8661.0000.0000.1550.0000.0001.0000.0000.8350.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
DLB0.0000.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0001.0001.0000.0720.0430.4700.0000.1001.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0001.0000.0001.0001.0001.0001.0000.0000.0001.0000.0001.0000.0360.0850.0000.0000.1090.0001.0001.000NaN0.0180.0500.0001.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
DLBIF0.0000.0001.0001.0000.0001.0001.000NaNNaNNaNNaN1.0000.0000.0000.0001.0000.0000.0000.0001.0000.0470.8090.0000.7001.0001.0001.0000.0001.0000.0001.0000.0000.000NaN1.0000.0001.0000.0000.0000.0001.0001.0001.0000.0001.0000.0000.0000.0000.000NaN0.0001.0000.0001.0000.0000.0001.0001.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
VASC0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0001.0001.0000.0000.0570.0000.0000.1900.0001.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0001.0000.0000.0001.0000.0001.0000.0000.0000.0001.0000.0000.3820.5631.000NaN0.0001.0000.0001.0000.0000.0000.0340.2670.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
VASCIF0.3720.3271.0001.0000.1541.0001.000NaNNaNNaNNaN1.0000.0000.0000.0001.0000.0000.0000.0001.0000.7081.0000.0000.0001.0000.0001.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0001.0001.0001.0000.0001.0000.0000.0000.0001.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0000.1431.0000.000NaN1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
VASCPS0.1890.0000.0000.0000.0000.0000.1040.2870.0000.2190.0000.0001.0001.0001.0000.0000.0000.0001.0000.1390.0000.0000.0000.0000.0001.0000.0001.0001.0001.0000.0001.0000.0000.0000.1371.0000.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1990.3541.0000.0000.0001.0000.0001.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
VASCPSIF0.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0001.0000.0000.0000.0001.0000.0001.0000.0001.0001.0000.0001.0000.0001.0001.0001.0000.0000.000NaN0.0001.0001.0000.0000.0000.0001.0001.0000.000NaN0.000NaN0.0000.0000.000NaN1.0000.0001.0001.0000.0001.0000.0001.0000.0000.337NaN1.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
ALCDEM0.0000.0000.0000.0770.0770.0000.0701.0001.0001.0001.0000.0601.0000.0000.0000.0860.0000.0000.0000.0000.0440.0000.0000.0000.0001.0000.0001.0000.0001.0001.0001.0000.0000.0000.0001.0000.0001.0001.0001.0000.0000.0000.0640.1340.0001.0000.0450.0740.0001.0000.0000.0001.0000.0001.0000.0620.3710.0001.0000.0200.1590.0001.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0001.0001.0000.0000.1330.0260.0000.0000.0000.0000.0000.0000.0810.1100.2670.0690.0000.0000.0000.0000.0001.0000.1430.4770.0000.0000.1360.333
ALCDEMIF0.0000.0001.0000.4900.6470.0001.0000.0000.0000.0000.0000.000NaNNaNNaN0.0001.0001.0001.0000.0000.000NaN0.0001.0001.0000.0001.0000.0001.0000.0001.0001.0000.000NaN1.0000.0001.0000.0000.0000.0001.0001.0000.2541.0001.0000.0000.2130.7411.0000.0000.0001.0000.0001.0000.0000.1551.0001.0000.0000.1551.0001.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.5841.0001.0000.0001.0000.0000.0000.000NaNNaNNaNNaNNaNNaN0.000NaN1.0000.0521.0000.0001.0000.0000.000NaN1.0000.0000.4681.0001.0000.2650.1501.000
DEMUN0.1210.0450.0000.0000.0000.0000.0540.0000.0000.0000.0000.1251.0001.0000.0000.0001.0001.0001.0000.2060.2401.0000.1470.2020.0000.0000.0001.0000.0000.0000.0000.0001.0001.0000.0001.0000.0001.0001.0001.0001.0000.0000.1850.0000.1310.3130.0710.1950.0000.0000.0060.0000.0001.000NaN0.0440.0000.0360.0000.0850.1310.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
DEMUNIF0.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0001.0001.0000.0000.0000.5540.000NaN1.0000.0000.000NaN0.000NaN1.0001.0001.0000.0001.0000.0000.0000.0001.0001.0000.0001.0000.0000.0000.0000.1830.000NaN0.0000.0001.0001.0000.0000.0001.0000.000NaN0.5050.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
FTD0.0000.0570.0890.0000.0000.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0001.0001.0000.0000.1221.0000.1000.0000.0001.0000.0001.0000.1370.0000.0001.0000.0001.0001.0001.0000.0001.0001.0001.0001.0000.0000.0000.2800.0001.0000.0000.0000.0001.0000.0000.0001.0001.000NaN0.0001.0000.0001.0000.0000.0780.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
FTDIF0.0000.9050.0000.0001.0001.0001.0000.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0001.0001.0000.0001.000NaN1.0000.0001.0000.0001.0001.0001.0000.0001.0000.0001.0001.0001.0000.0000.0000.0001.0001.0000.000NaN1.0000.0000.0001.0001.0000.0001.0001.000NaN1.0000.0001.0000.0001.0000.0000.000NaN1.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
PPAPH0.2720.0720.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.1500.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0000.0000.0001.0000.0000.0001.0000.0001.0000.0000.0000.0001.0000.0000.0001.0001.000NaN0.0141.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
PPAPHIF1.0000.3331.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaN0.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0001.0000.0000.0001.0001.0001.0000.0001.0000.0000.000NaN1.0000.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
PNAPH0.5000.8661.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaN0.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0001.0000.0001.0001.0001.0000.0001.0000.0000.0001.0001.0000.0001.0000.000NaN1.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
PPAOTHR0.5000.1771.0001.0000.000NaNNaN0.0000.0000.0000.000NaN0.0000.0000.000NaN0.0000.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.0001.0001.0001.0001.0000.0001.0000.0000.0001.0001.0000.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
PSP0.0000.0001.0000.0150.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0001.0000.0001.0000.0000.0000.0001.0000.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0850.0000.1330.8660.2440.0000.0000.0001.0000.0001.0000.0001.0000.8940.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.000
CORT0.0000.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
MEDS0.0310.0210.0000.1430.1430.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0000.1440.0000.0890.1740.0001.0000.0001.0000.0000.0000.0640.2540.1850.0000.0000.0000.0001.0001.0001.0000.0000.0001.0000.9860.1710.2720.0840.1000.0460.0000.0000.0000.0000.0001.0000.0400.0000.0130.0000.0830.2330.0000.0000.0220.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0330.0000.0000.0001.0001.0001.0001.0001.0001.0000.0260.0000.0000.0930.2670.0570.0000.0550.0480.0000.0001.0000.0820.1900.0000.0000.0001.000
MEDSIF0.0000.2671.0000.6810.5040.0000.0000.0000.0001.0000.0000.0001.0001.0000.0001.0000.0000.0000.0000.0000.4871.0000.0000.0001.0000.0001.0000.0000.000NaN0.1341.0000.0001.0000.280NaN1.0000.0000.0000.0001.0001.0000.9861.0000.2750.0000.1070.2560.0001.0001.0000.000NaN1.0000.0000.0000.0000.000NaN0.1540.4010.000NaN0.000NaN1.0001.0001.0001.0000.0001.0001.0001.0000.6351.0001.0000.0000.000NaNNaN1.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0001.0000.0001.0001.0000.0000.5141.0001.0001.0001.0000.000
DYSILL0.0510.0380.0000.1400.2080.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0001.0001.0000.0430.2210.0000.0630.0660.0001.0000.0001.0000.0000.0000.0001.0000.1310.0000.0001.0000.0001.0001.0001.0000.0000.0000.1710.2751.0000.0000.0770.1360.0100.0000.0000.0000.2580.0001.0000.0140.0000.0001.0000.0110.0050.0290.0000.0220.0000.0000.0000.0000.0001.0000.0000.0000.0000.0390.0300.0001.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0120.0001.0000.0001.0000.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.6400.0350.000
DYSILLIF0.0000.0001.0000.0000.5370.0000.2420.0000.0000.0001.0001.0000.0000.0000.0001.0000.0000.0000.0000.3630.1070.0000.2150.5781.0000.0001.0000.0000.000NaN1.0000.0000.3130.0001.0000.0001.0000.0000.0000.0001.0001.0000.2720.0000.0001.0000.0000.2770.0001.0000.0000.0891.0001.0000.0000.0001.0001.0000.0000.2651.0000.0001.0000.286NaN1.0001.0001.0001.0000.0001.0001.0001.0000.6710.9351.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0000.000NaN0.0001.0000.0001.0000.0000.000NaN1.0000.0000.000NaN
DEP0.0270.0550.0000.2200.0760.1520.0000.2890.0670.1610.0560.0000.0000.0000.0000.0560.0000.0000.0000.1040.1430.0870.0510.1390.0360.0000.0000.0000.0000.0000.0450.2130.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.1070.0770.0001.0000.0000.0640.2990.0200.0430.0000.0270.0000.0540.0000.0000.8160.0710.1150.0210.0000.0070.0000.0000.0000.0000.0650.8660.0530.0000.0000.1530.0000.0000.0000.0000.0000.6160.0240.2720.0000.0000.0000.0000.0000.0300.0000.0000.0290.0000.0380.0001.0000.0240.0000.0001.0000.1180.2060.0000.0000.0110.000
DEPIF0.1110.0000.0550.1860.4710.0490.0470.0000.0000.0000.2290.0001.0000.0000.0000.1830.0000.0000.0000.0000.4430.3400.1420.6980.0850.0000.0000.0000.0000.0000.0740.7410.1950.1830.0001.0000.000NaN1.0001.0000.0001.0000.1000.2560.1360.2770.0001.0000.0000.4870.0490.0430.0000.0001.0000.0650.0001.0001.0000.1180.3150.0000.8160.0341.0001.0000.0000.0000.0001.0000.0000.0981.0000.6180.5650.0450.0000.000NaN1.0000.0000.1920.0000.2940.0001.0001.0000.0001.0000.0000.0680.0000.0000.5300.0000.1271.0000.141NaN0.1930.4681.0000.0000.1271.000
OTHPSY0.0000.0340.0000.0290.0850.0000.0550.0000.0000.0000.2340.0001.0001.0001.0000.0001.0001.0001.0000.1880.0090.0000.0000.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0001.0000.0001.0001.0001.0000.0000.0000.0460.0000.0100.0000.0640.0001.0000.9510.0000.0000.0000.0001.0000.0111.0000.0001.0000.0190.0880.0001.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0001.0001.0000.0000.0000.0000.0000.2240.0000.0000.0000.0001.0000.0001.0000.0001.0000.0000.0091.0000.0001.0000.0420.1660.0001.0000.0001.000
OTHPSYIF0.3300.1941.0000.4180.3010.1510.6350.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.2680.4161.0000.0000.0000.000NaN1.0000.0000.000NaN1.0000.0000.000NaN1.0000.0001.0000.0000.0000.0001.0001.0000.0001.0000.0001.0000.2990.4870.9511.0001.0000.0001.0001.000NaN1.000NaN1.000NaN0.0000.0001.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0001.0000.0001.0000.0000.0000.408NaNNaNNaNNaNNaNNaN0.4591.0000.0001.0000.0001.0000.0001.0001.0000.0001.0000.0000.9131.0001.0000.0001.0000.000
PARK0.0640.0950.0000.1210.0520.0000.0000.0000.0000.0000.0000.0970.0000.0000.0000.0001.0001.0001.0000.0580.0520.0430.0000.0740.1090.0000.0001.0000.0001.0000.0000.0000.0060.0000.0001.0000.0001.0001.0001.0000.0000.0000.0001.0000.0000.0000.0200.0490.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.5690.0001.0000.0000.0000.0000.0001.0000.0000.0890.0000.1120.0000.1560.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0360.0001.0000.0001.0000.0001.0000.0000.0001.0000.0001.0000.0000.0000.0000.0000.0001.000
STROKE0.0350.0550.0000.0740.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0471.0001.0000.0000.0480.0000.0000.0000.0630.0001.0000.3820.0000.1990.0000.0001.0000.0000.0000.0001.0000.0001.0000.0001.0001.0000.0000.0000.0000.0000.0890.0430.0430.0000.0000.0001.0000.9740.0001.0000.0510.0000.0001.0000.0000.0490.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
STROKIF0.2510.3831.0000.2160.4280.2340.0000.0000.0000.0000.0001.0000.0000.0000.0000.000NaNNaNNaN0.0000.5311.0000.2780.8661.0000.0000.5630.0000.3541.0001.0000.0000.0001.0001.000NaN1.0000.000NaN0.0001.0001.0000.000NaN0.2581.0000.0000.0000.0001.0001.0000.9741.0001.000NaN0.0001.0001.000NaN0.000NaN0.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
HYCEPH0.0000.0080.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0001.0000.0001.0000.0270.0000.0001.0000.0000.0001.0001.0000.7070.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.0000.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.000
HYCEPHIF1.0001.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaNNaN0.0000.000NaN0.000NaN0.0000.0000.0001.0000.000NaN0.000NaN0.000NaN0.0000.0000.0001.0001.0001.0000.0001.0000.0000.0001.0001.000NaN1.0001.000NaN0.7071.0001.000NaN1.000NaN0.0000.0001.0000.0001.0000.000NaNNaNNaNNaN0.000NaNNaNNaNNaNNaNNaN0.000NaN0.0000.000NaN0.0000.0000.0000.0000.0000.000NaN0.0000.000NaN0.000NaN0.0000.000NaN0.000NaN0.000NaN0.000NaN0.000NaN0.000
BRNINJ0.0000.0170.0290.0390.0500.0000.0000.0000.0000.0000.0830.0001.0001.0001.0000.0001.0001.0001.0000.0830.1040.0000.0000.1550.0180.0000.0001.0000.0001.0000.0620.1550.0440.0000.0001.0000.0141.0001.0001.0000.0000.0000.0400.0000.0140.0000.0540.0650.0111.0000.0000.0510.0000.0001.0001.0000.6530.0001.0000.0290.0700.0001.0000.0001.0000.0000.0000.0750.0001.0000.0000.0000.1810.0000.0000.0001.0000.0001.0001.0000.0000.0000.0000.0000.2240.0000.0000.0290.0001.0000.1700.0000.0001.0000.0000.0070.0000.0001.0000.0130.1440.0000.0000.0001.000
BRNINJIF0.0000.0000.0000.0000.0000.0000.308NaNNaNNaNNaN1.0000.0000.0000.0001.0000.0000.0000.0000.0000.3461.0000.0000.0000.0501.0001.0000.0001.0000.0000.3711.0000.0001.0001.0000.0001.0000.0000.0000.0001.0001.0000.0000.0000.0001.0000.0000.0001.000NaN1.0000.0001.0001.000NaN0.6531.0001.000NaN0.3750.0001.0000.0001.0000.0001.0000.3160.0001.0000.0001.0001.0000.3160.4081.0001.0000.0001.0000.0000.0000.000NaNNaNNaNNaNNaNNaN0.0001.0000.5920.0000.0001.0000.0001.0000.548NaN1.0000.0000.1411.0001.0001.0001.0000.000
NEOP0.0000.0000.0000.0000.0160.0000.0001.0001.0001.0001.0000.0680.0000.0000.0000.0001.0001.0001.0000.0240.0000.0000.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0360.0000.0001.0000.0001.0001.0001.0000.0000.0000.0130.0000.0001.0000.0001.0000.0001.0000.0000.0001.0000.0001.0000.0001.0001.0000.8160.0000.0000.0001.0000.0001.0000.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.0001.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.0000.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.000
NEOPIF0.0000.0001.0001.0000.0001.0000.0000.0000.0000.0000.0000.000NaNNaNNaN1.0000.0000.0000.0000.0000.0001.0001.0001.0001.0000.0001.0000.0001.0000.0001.0000.0000.000NaN1.0000.0001.0000.0000.0000.0001.0001.0000.000NaN1.0000.0000.8161.0001.000NaN1.0001.000NaN1.000NaN1.000NaN0.8161.0000.0001.0001.0000.0001.0000.000NaN0.0000.0000.0000.0000.0000.0000.000NaN0.000NaN0.000NaN0.0000.000NaN0.0000.0000.0000.0000.0000.000NaN0.0000.000NaN0.000NaN0.0000.000NaN0.000NaN0.000NaN0.000NaN0.000NaN0.000
COGOTH0.0110.0080.0000.1800.2570.0000.0470.0000.0000.0000.0000.0620.0000.0000.0000.0000.0000.0000.0000.0000.2030.1340.0540.0000.0000.0000.0000.1430.0000.3370.0200.1550.0850.5050.0000.0000.0001.0001.0001.0000.0000.0000.0830.1540.0110.2650.0710.1180.0190.0000.0000.0000.0000.0000.0000.0290.3750.0000.0001.0000.0400.2390.4630.0070.0000.1520.0000.0000.0001.0000.0000.0000.0000.0180.0000.0001.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0140.0000.0000.0290.0000.0340.0000.0440.0001.0000.0001.0000.0160.0000.0000.0000.0001.000
COGOTHIF0.2570.2021.0000.2260.5210.1620.0000.0000.0000.0000.0000.1211.0000.0000.0830.0001.0001.0001.0000.0000.5580.0000.3190.8350.0000.0000.0001.0000.000NaN0.1591.0000.1310.0000.078NaN1.0000.0000.0000.0001.0001.0000.2330.4010.0051.0000.1150.3150.0880.0000.0000.049NaN1.0000.0000.0700.0000.0001.0000.0401.0000.0730.3370.000NaN0.0001.0001.0001.0000.0001.0001.0001.0000.3781.0001.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000NaNNaN0.1321.0000.1321.0001.0001.0000.0001.0000.0000.1430.7071.0001.0001.0000.000
COGOTH20.0000.0000.0000.0740.1120.0440.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0001.0000.0340.0000.0001.0000.0001.0000.0000.0000.0001.0000.0001.0001.0001.0000.0001.0000.0000.0000.0290.0000.0210.0000.0001.0000.0000.0000.0000.0001.0000.0001.0000.0001.0000.2390.0731.0000.9390.0530.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0001.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0000.0001.0000.0001.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.000
COGOTH2F0.0000.0001.0001.0000.5551.0000.0000.0000.0000.0001.0000.000NaNNaNNaN0.000NaNNaNNaN0.0000.0001.0000.4241.0001.0000.0000.267NaN1.0000.0001.0000.0000.0001.0001.0000.0001.0000.0000.0000.0001.0001.0000.000NaN0.0001.0000.0000.8161.0000.0000.5690.0001.0001.0000.0001.0000.0001.0000.0000.4630.3370.9391.0000.000NaN1.000NaNNaNNaN0.000NaNNaNNaN1.0000.0001.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000NaN0.0001.0000.0001.0000.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.000
COGOTH30.0000.0680.0000.0340.0760.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0001.0001.0000.0000.0061.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0000.0001.0000.0220.0000.0220.2860.0070.0340.0001.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0070.0000.0530.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0001.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.000
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dxmethod0.0000.0000.0000.0290.0450.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.000NaN0.0001.0000.000NaN0.1520.0000.0001.0000.0001.0001.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0001.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.0000.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.000
amndem0.0470.0000.0001.0001.0001.0001.0000.0000.000NaN0.0001.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0851.0000.0001.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.000NaN0.0000.3161.0000.0000.0001.0000.000NaN1.0000.0000.0001.0000.0440.4020.8660.2990.4020.1330.2110.0000.2490.4010.0851.0000.0000.0001.0001.0001.0001.0001.0001.0000.0540.0000.0000.0001.0000.0001.0000.0000.0001.0000.000NaN0.0001.0001.0001.0000.0001.000
pca0.0000.0000.0001.0001.0001.0001.0000.0000.000NaN0.0001.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.000NaN0.0750.0001.0000.0000.0001.0000.000NaN1.0000.0000.0000.0441.0000.0001.0000.0000.0840.0000.0000.0000.0340.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.0000.0000.0001.0000.0001.0000.0000.0001.0000.000NaN0.0001.0001.0001.0000.0001.000
ppasyn0.1420.0000.0001.0001.0001.0001.0000.0000.000NaN0.0001.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.1331.0000.0001.0000.0001.0000.0650.0000.0001.0000.0000.0000.0000.000NaN0.0001.0001.0000.0000.0001.0000.000NaN1.0000.0000.0000.4020.0001.0001.0000.4280.0000.0000.0550.0000.0001.0000.4281.0000.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.0000.0001.0000.0000.0001.0000.000NaN0.0001.0001.0001.0000.0001.000
ppasynt0.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.8661.0001.0000.0001.0000.0000.8661.0001.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.8661.0001.0001.0000.3541.0001.0000.5301.0001.0000.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0001.0000.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.000
ftdsyn0.0000.0000.0001.0001.0001.0001.0000.0000.000NaN0.0001.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.2441.0000.0001.0000.0001.0000.0530.0000.0001.0000.0000.0000.0000.000NaN0.0001.0001.0000.0000.0001.0000.000NaN1.0000.0000.0000.2990.0000.4280.3541.0000.0000.0000.0000.0000.0001.0000.2441.0000.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.0000.0001.0000.0000.0001.0000.000NaN0.0001.0001.0001.0000.0001.000
lbdsyn0.0000.0000.0001.0001.0001.0001.0000.0000.000NaN0.0001.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0001.0000.0000.0980.0001.0000.0890.0000.0000.000NaN0.0001.0001.0000.0000.0001.0000.000NaN1.0000.0000.0000.4020.0840.0001.0000.0001.0000.0000.1970.1470.5930.3460.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.0000.0001.0000.0000.0001.0000.000NaN0.0001.0001.0001.0000.0001.000
namndem0.0000.0000.0001.0001.0001.0001.0000.0000.000NaN0.0001.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.000NaN0.1810.3161.0000.0000.0001.0000.000NaN1.0000.0000.0000.1330.0000.0001.0000.0000.0001.0000.0541.0000.0001.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0490.0000.0000.0001.0000.0001.0001.0000.0001.0000.000NaN0.0001.0001.0001.0000.0001.000
alzdis0.1190.2390.0000.8500.6590.2060.1390.0000.2750.0000.0000.0561.0000.0000.0000.0730.0000.0000.0000.2800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5840.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.6350.0390.6710.1530.6180.0000.0000.1120.0000.0000.000NaN0.0000.4080.000NaN0.0180.3780.0001.0000.0001.0000.0000.2110.0000.0550.5300.0000.1970.0541.0001.0000.0590.7170.0000.0000.8940.0350.6370.0000.0000.0000.0000.0000.0170.1290.0000.0000.0000.0000.1490.0840.0000.0000.0001.0000.0000.6930.0000.0000.0000.333
alzdisif0.1550.0000.0000.0000.0000.0000.0000.0000.0990.0000.0001.000NaNNaNNaN1.000NaNNaNNaN0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0001.0000.0300.9350.0000.5650.0001.0000.0000.0000.0000.000NaN0.0001.0001.0000.0000.0001.0001.0000.0000.000NaN1.0000.0000.0000.0001.0000.0000.1471.0001.0001.0000.0440.2180.000NaNNaN0.1270.9640.0000.0000.0001.0001.0000.0001.0001.0000.0001.0000.1030.0000.0000.0001.0000.000NaN0.0001.0001.0001.0000.0001.000
lbdis0.0740.0460.0000.1210.0300.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0001.0000.0000.0450.0001.0000.1560.0000.0000.000NaN0.0001.0000.000NaN0.0001.0000.0001.0000.0001.0000.0000.2490.0340.0001.0000.0000.5930.0000.0590.0441.0001.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0180.2040.0001.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.000
lbdif0.3480.0000.0001.0000.000NaNNaNNaNNaNNaNNaNNaN0.0000.0000.000NaN0.0000.0000.000NaN0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.4010.0001.0000.0001.0000.3461.0000.7170.2181.0001.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000NaN1.0000.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.000
ftldnos0.0000.0000.0000.0620.0340.0000.0000.0000.0000.0000.0000.0421.0000.0000.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0330.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.000NaN0.0001.0000.000NaN0.0001.0000.0001.0000.0001.0000.0000.0850.0000.4280.0000.2440.0000.0000.0000.0000.0001.0001.0001.0000.8940.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.0000.0001.0000.0000.0001.0000.0001.0000.0000.0000.0001.0000.0001.000
ftldnoif0.0000.0000.0001.0000.0001.0001.000NaNNaNNaNNaN1.000NaNNaNNaN1.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.000NaN1.0000.0000.000NaN1.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.000NaN1.0000.0001.0001.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0001.0000.000NaN1.0000.0001.0000.0000.000NaN1.0000.0001.0000.000
ftldsubt0.0000.0000.0001.0000.5241.0001.000NaNNaNNaNNaN1.000NaNNaNNaN1.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.8941.0000.000NaN1.0000.0000.6161.0001.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0001.0000.894NaN1.0000.0000.8940.0001.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0001.0000.0001.0001.0000.0001.0000.0000.000NaN1.0000.0001.0000.000
cvd0.0190.0900.0000.1130.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.2300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0001.0000.0240.0000.0000.4080.0000.0000.0000.000NaN0.0000.0000.000NaN0.0001.0000.0001.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0350.1270.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0001.000
cvdif0.0000.1390.0000.1330.0460.5000.0001.0000.0000.0000.0001.0000.0000.0000.0000.3310.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.133NaN0.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0000.0000.2720.1920.000NaN1.0000.0000.0001.0000.0000.000NaN1.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.6370.9641.0000.0001.0000.0000.0001.0001.0000.0000.7340.2160.1330.1330.0421.000NaN1.0000.0001.0000.0000.0000.000NaN1.0000.0000.000NaN1.000NaN1.0000.000
prevstk0.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.026NaN0.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0000.0000.0000.0000.000NaN1.0000.0000.0001.0000.0000.000NaN1.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.0000.0001.0000.0001.0000.0000.0001.0000.0001.0000.0000.0000.0000.0000.0001.000NaN1.0000.0001.0000.0000.0000.000NaN1.0000.0000.000NaN1.000NaN1.0000.000
strokdec0.1630.3880.0000.6110.0000.0000.1891.0001.0001.0001.0001.0000.0000.0000.0000.4551.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaN0.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0000.0000.0000.2940.000NaN1.0000.0000.0001.0000.0000.000NaN1.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.0000.0001.0000.0001.0000.0000.0001.0000.7340.0001.0000.0000.0000.0000.0001.000NaN1.0000.0001.0000.0000.0000.000NaN1.0000.0000.000NaN1.000NaN1.0000.000
stkimag0.0000.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaN0.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0000.0000.0000.0000.224NaN1.0000.0000.0001.0000.0000.224NaN1.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.0000.0001.0000.0001.0000.0000.0001.0000.2160.0000.0001.0000.7130.7130.0001.000NaN1.0000.0001.0000.0000.0000.000NaN1.0000.0000.224NaN1.000NaN1.0000.000
infnetw0.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaN0.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0000.0000.0001.0000.000NaN1.0000.0000.0001.0000.0000.000NaN1.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0001.0000.0001.0000.0000.0001.0000.1330.0000.0000.7131.0000.7370.0001.000NaN1.0000.0001.0000.0001.0000.000NaN1.0000.0000.000NaN1.000NaN1.0000.000
infwmh0.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaN0.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0000.0000.0001.0000.000NaN1.0000.0000.0001.0000.0000.000NaN1.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0001.0000.0001.0000.0000.0001.0000.1330.0000.0000.7130.7371.0000.0001.000NaN1.0000.0001.0000.0001.0000.000NaN1.0000.0000.000NaN1.000NaN1.0000.000
esstrem0.0620.0700.0000.0280.0000.0000.0000.0000.0000.0000.3210.0001.0001.0001.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0260.0000.0120.0000.0300.0000.0000.4590.0360.0000.0000.000NaN0.0290.0000.000NaN0.0140.0000.0000.0000.0001.0000.0000.0540.0000.0001.0000.0000.0000.0490.0170.0000.0001.0000.0001.0001.0000.0000.0420.0000.0000.0000.0000.0001.0001.0000.0000.0001.0000.0001.0000.0470.0000.0000.0001.0000.0370.2180.0060.0000.0001.000
esstreif0.0000.0000.0000.0000.1640.0000.0001.0001.0000.0000.0001.0000.0000.0000.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaN0.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0001.0000.0000.0001.0001.0000.0000.000NaN0.000NaN1.0000.0001.0000.0001.0001.0000.0001.0001.0000.0000.1291.0001.0000.0001.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.0001.0000.000NaN1.0000.0000.0001.0000.000NaN1.0000.000
brnincte0.1910.0000.0000.0000.0000.0000.000NaNNaNNaNNaN1.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0811.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0001.0000.0000.0000.0001.0000.0001.0000.0000.0001.0000.0001.0000.5921.0000.0000.000NaN1.0000.0001.0000.0001.0000.0000.0001.0000.0001.0001.0000.0000.0001.0001.0000.0001.0000.0000.0000.000NaNNaNNaNNaNNaNNaN0.0001.0001.0000.0000.0001.0000.0001.0000.000NaN1.0000.0000.0001.0001.0001.0001.0000.000
epilep0.0940.0430.0000.0380.0560.0000.0211.0001.0001.0001.0000.0001.0000.0000.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1100.0520.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0930.0000.0001.0000.0290.0680.0001.0000.0000.0000.0000.000NaN0.1700.0000.000NaN0.0290.1320.0001.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0180.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.0000.0001.0001.0000.0001.0000.0000.0070.0000.0001.0000.0400.2410.0000.0000.0001.000
epilepif0.2830.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000NaNNaNNaN1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2671.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.2670.0001.0000.0000.0000.0001.0000.0001.0000.0000.0001.0000.0000.0000.0001.0000.0000.0001.0001.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0000.204NaN1.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0001.0001.0000.0001.0000.204NaN1.0000.0000.2671.0001.0001.0001.0000.000
othcog0.0000.0000.0000.1300.1950.0510.0590.0000.2130.0000.0000.0001.0001.0001.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0570.0000.0000.0000.0380.0000.0001.0000.0000.0000.0000.000NaN0.0001.0000.000NaN0.0340.1320.0001.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.1030.0001.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.0001.0001.0000.0000.0001.0000.0001.0000.0190.1440.0000.0000.0001.000
othcogif0.2630.0000.0001.0000.1490.0590.0000.0000.0000.0001.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0001.0000.000NaN0.0000.5301.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0000.0001.0001.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.1490.0001.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0001.0001.0000.0001.0000.0001.0000.0000.0001.0001.0001.0001.0000.000
deptreat0.0000.1370.0000.0610.0000.0000.2220.0000.0000.0000.0000.0001.0001.0001.0000.0000.0000.0000.0000.1010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0551.0000.0000.0001.0000.0000.0001.0000.0000.0000.0001.0000.0000.0001.0001.0000.0000.0441.0000.0001.0000.000NaN1.0000.0000.0000.0001.0000.0000.0001.0000.0840.0000.0001.0000.000NaN1.0000.0000.0000.0000.0000.0001.0001.0000.0471.0001.0000.0001.0000.0000.0001.0000.0001.0000.000NaN0.0320.6271.0000.0000.0001.000
bipoldx0.0000.0530.0000.0000.0340.0000.0001.0001.0001.0001.0000.0421.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0480.0000.0001.0000.0240.1270.0091.0000.0000.0000.0000.000NaN0.0070.5480.000NaN0.0001.0000.0001.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.2040.0001.0000.0001.0001.0000.0001.0000.1040.0000.0000.0000.0001.000
bipoldif0.0000.0000.0001.0000.0001.0001.0000.0000.0000.0000.0000.000NaNNaNNaN0.000NaNNaNNaN0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaN0.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0001.0001.0000.0000.0001.0001.0000.0001.0000.0000.0001.0000.0000.000NaN1.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0001.0000.0001.0000.0000.0000.000NaNNaNNaNNaNNaNNaN0.000NaNNaN0.000NaN1.0000.0001.0001.0001.0001.0000.0000.0001.0001.000NaN1.0000.000
schizop0.0000.0860.0000.0000.0000.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0001.0000.0000.1410.0001.0000.0000.0000.0000.000NaN0.0001.0000.000NaN0.0001.0000.0001.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.0000.0001.0000.0000.0001.0001.0001.0000.0001.0000.0001.0000.0001.000
schizoif1.0001.0000.0001.0001.000NaNNaN0.0000.0000.0000.000NaN0.0000.0000.000NaN0.0000.0000.000NaN0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0000.0001.000NaN1.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.000NaNNaNNaN0.000NaNNaNNaN1.000NaN1.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0001.0000.000NaN1.0000.0001.0001.0001.0000.0001.0000.0001.0000.000
anxiet0.0250.0420.0000.0890.1600.0920.0000.0000.0320.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1430.4680.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0820.5140.0000.0000.1180.1930.0420.9130.0000.0000.0000.000NaN0.0130.1410.000NaN0.0160.1430.0001.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.2240.0000.0000.0370.0000.0000.0400.2670.0190.0000.0320.1040.0000.0001.0001.0001.0000.0000.1850.0000.000
anxietif0.0000.2330.0001.0000.6510.4330.0000.6160.5240.3161.0000.0001.0001.0001.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4771.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.1901.0000.000NaN0.2060.4680.1661.0000.0000.0000.0001.0000.0000.1441.0001.0000.0000.0000.7071.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.6931.0001.0000.0000.000NaNNaN0.000NaNNaNNaNNaNNaNNaN0.2181.0001.0000.2411.0000.1441.0000.6270.0001.0001.0000.0001.0001.0001.0001.0000.000NaN
ptsddx0.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.000NaN0.0001.0000.000NaN0.0001.0000.0001.0000.0001.0000.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.0001.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0060.0001.0000.0001.0000.0001.0001.0000.0001.0000.0001.0000.0001.0001.0001.0000.0001.000
alcabuse0.0000.0000.0000.6220.0000.0001.0000.0000.0000.0000.0000.000NaNNaNNaN0.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2650.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0001.0000.6400.0000.0000.0001.0000.0000.0000.0000.0001.0000.0000.0001.0001.0000.0000.0001.0001.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0001.0000.0001.0000.0000.0000.000NaNNaNNaNNaNNaNNaN0.000NaN1.0000.0001.0000.0001.0000.0000.000NaN1.0000.0000.1851.0001.0001.0000.0001.000
impsub0.0000.0120.0000.0620.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1360.1500.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0350.0000.0110.1270.0001.0000.0000.0000.0000.000NaN0.0001.0000.000NaN0.0001.0000.0001.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.0000.0001.0000.0000.0001.0000.0001.0000.0000.0000.0000.0001.0001.000
impsubif0.5000.0000.0001.0000.0001.0001.000NaNNaNNaNNaN1.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3331.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0000.000NaN0.0001.0001.0000.0001.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0000.3331.0001.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0001.0000.0001.0001.0000.0001.0000.0000.000NaN1.0001.0001.0001.000

Missing values

2023-10-17T23:47:20.433791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-17T23:47:22.334083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-17T23:47:30.981899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

OASISIDOASIS_session_labeldays_to_visitage at visitWHODIDDXNORMCOGDEMENTEDMCIAMEMMCIAPLUSMCIAPLANMCIAPATTMCIAPEXMCIAPVISMCINON1MCIN1LANMCIN1ATTMCIN1EXMCIN1VISMCINON2MCIN2LANMCIN2ATTMCIN2EXMCIN2VISIMPNOMCIPROBADPROBADIFPOSSADPOSSADIFDLBDLBIFVASCVASCIFVASCPSVASCPSIFALCDEMALCDEMIFDEMUNDEMUNIFFTDFTDIFPPAPHPPAPHIFPNAPHSEMDEMANSEMDEMAGPPAOTHRPSPPSPIFCORTCORTIFHUNTHUNTIFPRIONPRIONIFMEDSMEDSIFDYSILLDYSILLIFDEPDEPIFOTHPSYOTHPSYIFDOWNSDOWNSIFPARKSTROKESTROKIFHYCEPHHYCEPHIFBRNINJBRNINJIFNEOPNEOPIFCOGOTHCOGOTHIFCOGOTH2COGOTH2FCOGOTH3COGOTH3Fhivdxmethodamndempcappasynppasyntftdsynlbdsynnamndemamylpetamylcsffdgadhippatrtaupetadcsftaufdgftldtpetftldmrftlddatscanothbiomimaglinfimaglacimagmachimagmichimagmwmhimagewmhadmutftldmutothmutalzdisalzdisiflbdislbdifmsamsaifftldmoftldmoifftldnosftldnoifftldsubtcvdcvdifprevstkstrokdecstkimaginfnetwinfwmhesstremesstreifbrnincteepilepepilepifneopstathivifothcogothcogifdeptreatbipoldxbipoldifschizopschizoifanxietanxietifdelirdelirifptsddxptsddxifalcabuseimpsubimpsubif
0OAS30001OAS30001_UDSd1_d0000065.191.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1OAS30001OAS30001_UDSd1_d033933966.121.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2OAS30001OAS30001_UDSd1_d072272267.171.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3OAS30001OAS30001_UDSd1_d1106110668.221.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4OAS30001OAS30001_UDSd1_d1456145669.181.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5OAS30001OAS30001_UDSd1_d1894189470.381.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6OAS30001OAS30001_UDSd1_d2181218171.171.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7OAS30001OAS30001_UDSd1_d2699269972.591.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8OAS30001OAS30001_UDSd1_d3025302573.481.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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OASISIDOASIS_session_labeldays_to_visitage at visitWHODIDDXNORMCOGDEMENTEDMCIAMEMMCIAPLUSMCIAPLANMCIAPATTMCIAPEXMCIAPVISMCINON1MCIN1LANMCIN1ATTMCIN1EXMCIN1VISMCINON2MCIN2LANMCIN2ATTMCIN2EXMCIN2VISIMPNOMCIPROBADPROBADIFPOSSADPOSSADIFDLBDLBIFVASCVASCIFVASCPSVASCPSIFALCDEMALCDEMIFDEMUNDEMUNIFFTDFTDIFPPAPHPPAPHIFPNAPHSEMDEMANSEMDEMAGPPAOTHRPSPPSPIFCORTCORTIFHUNTHUNTIFPRIONPRIONIFMEDSMEDSIFDYSILLDYSILLIFDEPDEPIFOTHPSYOTHPSYIFDOWNSDOWNSIFPARKSTROKESTROKIFHYCEPHHYCEPHIFBRNINJBRNINJIFNEOPNEOPIFCOGOTHCOGOTHIFCOGOTH2COGOTH2FCOGOTH3COGOTH3Fhivdxmethodamndempcappasynppasyntftdsynlbdsynnamndemamylpetamylcsffdgadhippatrtaupetadcsftaufdgftldtpetftldmrftlddatscanothbiomimaglinfimaglacimagmachimagmichimagmwmhimagewmhadmutftldmutothmutalzdisalzdisiflbdislbdifmsamsaifftldmoftldmoifftldnosftldnoifftldsubtcvdcvdifprevstkstrokdecstkimaginfnetwinfwmhesstremesstreifbrnincteepilepepilepifneopstathivifothcogothcogifdeptreatbipoldxbipoldifschizopschizoifanxietanxietifdelirdelirifptsddxptsddxifalcabuseimpsubimpsubif
8489OAS31470OAS31470_UDSd1_d041941966.06NaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.01.0NaNNaNNaNNaNNaNNaNNaN8.08.08.08.08.08.08.08.08.08.00.08.08.08.08.08.08.09.09.09.00.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaN0.0NaNNaNNaNNaNNaNNaN0.0NaNNaN0.0NaNNaNNaN0.0NaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaN0.0NaN
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8491OAS31471OAS31471_UDSd1_d045745766.55NaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.01.0NaNNaNNaNNaNNaNNaNNaN8.08.08.08.08.08.08.08.08.08.00.08.08.08.08.08.08.09.09.09.00.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaN0.0NaNNaNNaNNaNNaNNaN0.0NaNNaN0.0NaNNaNNaN0.0NaNNaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaNNaN0.0NaN
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